CN112492379B - Audio and video multi-path concurrent decoding method and device and computer equipment - Google Patents

Audio and video multi-path concurrent decoding method and device and computer equipment Download PDF

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CN112492379B
CN112492379B CN201910867071.4A CN201910867071A CN112492379B CN 112492379 B CN112492379 B CN 112492379B CN 201910867071 A CN201910867071 A CN 201910867071A CN 112492379 B CN112492379 B CN 112492379B
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decoding
audio
session
load degree
video
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CN112492379A (en
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徐珠宝
张玉晓
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Dawning Information Industry Beijing Co Ltd
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Dawning Information Industry Beijing Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
    • H04N21/439—Processing of audio elementary streams
    • H04N21/4398—Processing of audio elementary streams involving reformatting operations of audio signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
    • H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs
    • H04N21/4402—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs involving reformatting operations of video signals for household redistribution, storage or real-time display
    • H04N21/440218—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs involving reformatting operations of video signals for household redistribution, storage or real-time display by transcoding between formats or standards, e.g. from MPEG-2 to MPEG-4
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
    • H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
    • H04N21/4424—Monitoring of the internal components or processes of the client device, e.g. CPU or memory load, processing speed, timer, counter or percentage of the hard disk space used

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  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Databases & Information Systems (AREA)
  • Compression Or Coding Systems Of Tv Signals (AREA)

Abstract

The invention discloses an audio and video multi-path concurrent decoding method, an audio and video multi-path concurrent decoding device and computer equipment. The audio and video multi-path concurrent decoding method comprises the following steps: acquiring decoding parameters and use data of system resources for decoding audio and video, wherein the decoding parameters comprise: decoding concurrency number and soft decoding session number, the system resource includes at least one of the following: CPU occupancy rate, memory occupancy rate, system decoding load degree and session occupancy rate; adjusting decoding parameters by monitoring the use data of system resources; and decoding the audio and video based on the adjusted decoding parameters. According to the audio and video multi-path concurrent decoding method, the decoding parameters for decoding the audio and video and the use data of the system resources are obtained, the decoding parameters are adjusted by monitoring the use data of the system resources, and the audio and video are decoded based on the adjusted decoding parameters, so that the system hardware resources are fully utilized, the decoding efficiency is improved, and the stable operation of the system is ensured.

Description

Audio and video multi-path concurrent decoding method and device and computer equipment
Technical Field
The invention relates to the technical field of audio and video decoding, in particular to an audio and video multi-path concurrent decoding method, an audio and video multi-path concurrent decoding device and computer equipment.
Background
The audio and video decoding technology can be divided into a hard solution and a soft solution. The hard solution is independent of a CPU, and independently completes the decoding of the audio and video through a special hardware device, while the soft solution is to decode the audio and video through decoding software by using the CPU. In practical applications, hard decoding resources are often limited and only specific format audio-video decoding is supported. Therefore, the decoding software can be used for carrying out soft decoding on the audio and video so as to improve the decoding capability and support various audio and video formats. However, soft decoding may occupy a large amount of CPU resources, and especially for high-definition video of a high compression rate encoding algorithm, more CPU resources may be consumed. In some scenes requiring real-time analysis or synchronous playing of multiple paths of contents, the decoding capability of decoding software is required to be higher, and decoding processes or threads need to be developed as much as possible. And the multi-channel audio and video decoding needs to allocate data buffers before and after decoding for each channel of session, and extra space is also allocated for storing intermediate decoding data in the scene supporting interrupted decoding or segmented decoding. If the number of sessions is too large, a lot of system resources such as a CPU and a memory may be occupied, thereby affecting the system operation. Currently, decoding software only supports a fixed number of decoding concurrency numbers and session numbers, or can only adjust decoding concurrency numbers and session numbers through command lines or configuration files. When the decoding software runs on a new hardware configuration platform, the adjustment parameters (decoding concurrency number and session number) need to be reevaluated. And after determining the parameters, the decoding software cannot modify them once the decoding process is started. However, in most service scenarios, the original data stream to be decoded is constantly changing, which may cause problems of insufficient utilization of system resources or overload. If the hardware resources of the system are not fully utilized, namely a CPU or a memory is idle, a large amount of data can be queued for processing, and the efficiency is low; if the use of system hardware resources is out of limit, the system can be caused to operate in a stuck state or other exceptions. Therefore, there is a need for a decoding method for multiple concurrent audios and videos, which can ensure stable operation of a system while fully utilizing system hardware resources and improving decoding efficiency.
Disclosure of Invention
The object of the present invention is to solve at least to some extent one of the above mentioned technical problems.
Therefore, a first objective of the present invention is to provide an audio/video multi-channel concurrent decoding method, which can fully utilize system hardware resources, improve decoding efficiency, and ensure stable operation of the system.
A second object of the present invention is to provide an audio/video multi-channel concurrent decoding apparatus.
A third object of the invention is to propose a computer device.
A fourth object of the invention is to propose a non-transitory computer-readable storage medium.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides an audio and video multi-channel concurrent decoding method, where the method includes:
acquiring decoding parameters and use data of system resources for decoding audio and video, wherein the decoding parameters comprise: decoding concurrency number and soft decoding session number, the system resource includes at least one of: CPU occupancy rate, memory occupancy rate, system decoding load degree and session occupancy rate;
adjusting the decoding parameters by monitoring usage data of the system resources;
and decoding the audio and video based on the adjusted decoding parameters.
Optionally, the system decoding load degree is calculated based on the decoding load degree of each session.
Optionally, the session occupancy rate is calculated based on the current cached data session number and the soft decoding session number.
Optionally, the calculating the system decoding load degree based on the decoding load degree of each session includes:
calculating the decoding load degree of each path of session;
and calculating the average value of the decoding load degrees of all the conversations based on the decoding load degree of each conversation, and taking the average value as the system decoding load degree.
Optionally, when the current session is video decoding, calculating a decoding load degree of each session, including:
acquiring a video refresh rate (FPS), decoding duration, the number of decoding output frames and a video decoding weighted value of a current session, wherein the video decoding weighted value is in direct proportion to video resolution and video coding complexity;
and calculating the decoding load degree of the current session according to the video refresh rate FPS, the decoding duration, the decoding output frame quantity and the video decoding weighted value.
Optionally, when the current session is audio decoding, calculating a decoding load degree of each session, including:
acquiring the audio code rate, the decoding duration, the audio decoding byte number and the audio decoding weighted value of the current session;
and calculating the decoding load degree of the current conversation according to the audio code rate, the decoding duration, the audio decoding byte number and the audio decoding weighted value, wherein the audio decoding weighted value is in direct proportion to the audio coding complexity.
Optionally, the adjusting the decoding parameter by monitoring the usage data of the system resource includes:
judging whether the system decoding load degree is greater than the maximum decoding load degree or not, and whether the CPU occupancy rate is less than a CPU distributable threshold or not;
and if the system decoding load degree is greater than the maximum decoding load degree and the CPU occupancy rate is less than the CPU assignable threshold value, increasing the decoding concurrency number.
Optionally, the adjusting the decoding parameter by monitoring the usage data of the system resource includes:
judging whether the decoding load degree of the system is greater than the maximum decoding load degree or not, and whether the soft decoding conversation number is greater than the minimum supporting conversation number or not;
and if the system decoding load degree is greater than the maximum decoding load degree and the soft decoding conversation number is greater than the minimum supporting conversation number, reducing the soft decoding conversation number.
Optionally, the adjusting the decoding parameter by monitoring the usage data of the system resource includes:
judging whether the system decoding load degree is smaller than the minimum decoding load degree, whether the session occupancy rate is larger than the maximum session occupancy threshold value, and whether the memory occupancy rate is smaller than the memory allocable threshold value;
and if the system decoding load degree is smaller than the minimum decoding load degree, the session occupancy rate is larger than the maximum session occupancy threshold value, and the memory occupancy rate is smaller than the memory allocable threshold value, increasing the number of soft decoding sessions.
Optionally, the adjusting the decoding parameter by monitoring the usage data of the system resource includes:
judging whether the memory occupancy rate is greater than a memory upper limit threshold value or not, and whether the soft decoding conversation number is greater than a minimum support conversation number or not;
and if the memory occupancy rate is greater than the memory upper limit threshold value and the soft decoding conversation number is greater than the minimum supported conversation number, reducing the soft decoding conversation number.
Optionally, the adjusting the decoding parameter by monitoring the usage data of the system resource includes:
judging whether the CPU occupancy rate is greater than a CPU upper limit threshold value or not, and whether the decoding concurrency number is greater than the minimum supported decoding concurrency number or not;
and if the CPU occupancy rate is greater than the CPU upper limit threshold value and the decoding concurrency number is greater than the minimum supported decoding concurrency number, reducing the decoding concurrency number.
According to the audio and video multi-path concurrent decoding method, the decoding parameters for decoding the audio and video and the use data of the system resources are obtained, the use data of the system resources are monitored, the decoding parameters are adjusted, and the audio and video are decoded based on the adjusted decoding parameters, so that the system hardware resources are fully utilized, the decoding efficiency is improved, and the stable operation of the system is ensured.
In order to achieve the above object, a second embodiment of the present invention provides an audio/video multi-channel concurrent decoding apparatus, including:
an obtaining module, configured to obtain a decoding parameter for decoding an audio/video and usage data of a system resource, where the decoding parameter includes: decoding concurrency number and soft decoding session number, the system resource includes at least one of: CPU occupancy rate, memory occupancy rate, system decoding load degree and session occupancy rate;
the adjusting module is used for adjusting the decoding parameters by monitoring the use data of the system resources;
and the decoding module is used for decoding the audio and video based on the adjusted decoding parameters.
Optionally, the obtaining module is configured to:
and calculating the system decoding load degree based on the decoding load degree of each session.
Optionally, the obtaining module is configured to:
and calculating the session occupancy rate based on the current cached data session number and the soft decoding session number.
Optionally, the obtaining module is configured to:
calculating the decoding load degree of each path of session;
and calculating the average value of the decoding load degrees of all the conversations based on the decoding load degree of each conversation, and taking the average value as the system decoding load degree.
Optionally, the obtaining module is specifically configured to:
when the current session is video decoding, acquiring a video refresh rate (FPS), decoding duration, decoding output frame number and a video decoding weighted value of the current session, wherein the video decoding weighted value is in direct proportion to video resolution and video encoding complexity;
and calculating the decoding load degree of the current session according to the video refresh rate FPS, the decoding duration, the decoding output frame quantity and the video decoding weighted value.
Optionally, the obtaining module is specifically configured to:
when the current conversation is audio decoding, acquiring the audio code rate, the decoding duration, the audio decoding byte number and the audio decoding weighted value of the current conversation;
and calculating the decoding load degree of the current conversation according to the audio code rate, the decoding duration, the audio decoding byte number and the audio decoding weighted value, wherein the audio decoding weighted value is in direct proportion to the audio coding complexity. Optionally, the adjusting module is configured to:
judging whether the system decoding load degree is greater than the maximum decoding load degree or not, and whether the CPU occupancy rate is less than a CPU distributable threshold or not;
and if the system decoding load degree is greater than the maximum decoding load degree and the CPU occupancy rate is less than the CPU assignable threshold value, increasing the decoding concurrency number.
Optionally, the adjusting module is configured to:
judging whether the decoding load degree of the system is greater than the maximum decoding load degree or not, and whether the soft decoding conversation number is greater than the minimum supporting conversation number or not;
and if the system decoding load degree is greater than the maximum decoding load degree and the soft decoding conversation number is greater than the minimum supporting conversation number, reducing the soft decoding conversation number.
Optionally, the adjusting module is configured to:
judging whether the system decoding load degree is smaller than the minimum decoding load degree, whether the session occupancy rate is larger than the maximum session occupancy threshold value, and whether the memory occupancy rate is smaller than the memory allocable threshold value;
and if the system decoding load degree is smaller than the minimum decoding load degree, the session occupancy rate is larger than the maximum session occupancy threshold value, and the memory occupancy rate is smaller than the memory allocable threshold value, increasing the number of soft decoding sessions.
Optionally, the adjusting module is configured to:
judging whether the memory occupancy rate is greater than a memory upper limit threshold value or not, and whether the soft decoding conversation number is greater than the minimum support conversation number or not;
and if the memory occupancy rate is greater than the memory upper limit threshold value and the soft decoding conversation number is greater than the minimum supported conversation number, reducing the soft decoding conversation number.
Optionally, the adjusting module is configured to:
judging whether the CPU occupancy rate is greater than a CPU upper limit threshold value or not, and whether the decoding concurrency number is greater than a minimum supported decoding concurrency number or not;
and if the CPU occupancy rate is greater than the CPU upper limit threshold value and the decoding concurrency number is greater than the minimum supported decoding concurrency number, reducing the decoding concurrency number.
The audio and video multi-path concurrent decoding device provided by the embodiment of the invention can be used for adjusting the decoding parameters by acquiring the decoding parameters for decoding the audio and video and the use data of the system resources and monitoring the use data of the system resources, and decoding the audio and video based on the adjusted decoding parameters, thereby fully utilizing the hardware resources of the system, improving the decoding efficiency and ensuring the stable operation of the system.
In order to achieve the above object, an embodiment of a third aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the audio/video multi-channel concurrent decoding method according to the embodiment of the first aspect.
In order to achieve the above object, a non-transitory computer-readable storage medium is further provided in an embodiment of a fourth aspect of the present invention, and is characterized in that the computer program is configured to implement, when executed by a processor, the audio/video multi-channel concurrent decoding method according to the embodiment of the first aspect.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of an audio-video multi-path concurrent decoding method according to an embodiment of the present invention;
FIG. 1a is a flow chart of a computing system decoding load level according to one embodiment of the present invention;
fig. 2 is a flowchart of an audio-video multi-channel concurrent decoding method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an audio/video multi-channel concurrent decoding apparatus according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
The present invention is described in further detail below with reference to specific examples, which are not to be construed as limiting the scope of the invention as claimed.
The audio and video multi-path concurrent decoding method, device and computer equipment of the embodiment of the invention are described below with reference to the attached drawings.
For decoding of multi-channel concurrent audio and video, the requirement on the decoding capability of decoding software is high, and decoding processes or threads need to be developed as much as possible. And multi-channel audio and video decoding needs to allocate data buffers before and after decoding and extra space for each channel of conversation. If the number of sessions is too large, a lot of system resources such as a CPU and a memory are occupied, and therefore, the system operation is influenced. Currently, decoding software only supports a fixed number of decoding concurrency numbers and session numbers, or can only adjust decoding concurrency numbers and session numbers through command lines or configuration files. When the decoding software runs on a new hardware configuration platform, the adjustment parameters (decoding concurrency number and session number) need to be reevaluated. And after determining the parameters, the decoding software cannot modify them once the decoding process is started. However, in most service scenarios, the original data stream to be decoded is constantly changing, which may cause problems of insufficient utilization of system resources or overload. If the hardware resources of the system are not fully utilized, namely a CPU or a memory is idle, a large amount of data can be queued for processing, and the efficiency is low; if the resource usage is out of limits, system operation can be stuck or otherwise abnormal. Therefore, in order to solve the existing problems, the invention provides an audio and video multi-path concurrent decoding method which can ensure the stable operation of a system while fully utilizing system hardware resources and improving decoding efficiency.
Fig. 1 is a flowchart of audio-video multi-channel concurrent decoding according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps:
s1, acquiring decoding parameters for decoding audio and video and using data of system resources.
The decoding parameters include a decoding concurrency number and a soft decoding session number, which may be preset. For example, before the decoding starts, the decoding concurrency number and the soft decoding session number may be set according to the system hardware condition, such as the performance or usage of the CPU and the memory. Of course, the settings may also be entered manually.
The system resources include at least one of: CPU occupancy, memory occupancy, system decoding load and session occupancy. The CPU occupancy rate and the memory occupancy rate can be obtained through a monitoring tool such as a task manager of the Windows system. And the system decoding load degree and the session occupancy rate need to be calculated. The session occupancy rate is calculated by recording the number of the current cached data sessions and the number of soft decoding sessions. And the system decoding load degree is calculated based on the decoding load degree of each session.
As shown in fig. 1a, calculating the system decoding load may include the steps of:
and S11, calculating the decoding load degree of each channel of conversation.
Here, the decoding load degree can be divided into two cases. In the first case: and if a certain session is video decoding, calculating the decoding load degree of the current session according to the related parameters of the video. In the second case: and if a certain session is audio decoding, calculating the decoding load degree of the current session according to the relevant parameters of the audio.
In the first case:
firstly, acquiring a video refresh rate FPS, decoding duration, decoding output frame quantity and a video decoding weighted value of a current session. Wherein the video decoding weighting value is proportional to a video resolution and a video encoding complexity. Under the same coding format, the decoding weighted value of the high-resolution video is greater than that of the low-resolution video; the decoding weight value of the video with high encoding complexity is greater than the decoding weight value of the video with low encoding complexity.
After the video parameters are obtained, the decoding load degree of the video session can be calculated according to the video refresh rate FPS, the decoding duration, the number of decoding output frames and the video decoding weighted value. For example, for a certain video session i, the decoding load degree LRi corresponding to the certain video session i can be calculated by using the formula one.
The formula I is as follows: LRi = Wi FPS/(Fi/Ti)
Wherein, LRi is decoding load, wi is video decoding weighted value, FPS is video refresh rate, fi is decoding output frame number, and Ti is decoding duration (unit is second).
In the second case:
firstly, acquiring an audio code rate, decoding duration, audio decoding byte number and an audio decoding weighted value of a current session, and then calculating the decoding load degree of the current session according to the audio code rate, the decoding duration, the audio decoding byte number and the audio decoding weighted value. Wherein the audio decoding weighting value is proportional to the audio encoding complexity.
For a certain audio session j, the decoding load LRj corresponding to the audio session j can be calculated by using the formula two.
The formula II is as follows: LRj = Wj bps/(Bj 8/Tj)
Wherein LRj is decoding load, wj is audio decoding weighted value, bps is audio code rate, bj is audio decoding byte number, and Tj is decoding duration (unit is second). Where the audio code rate bps = sample rate x sample bit depth x number of channels.
Since audio decoding consumes much less resources than video decoding, the decoding weight Wj for all audio sessions should be multiplied by a factor m smaller than 1 based on its original value.
And S12, calculating the average value of the decoding load degrees of all the conversations based on the decoding load degree of each conversation, and taking the average value as the system decoding load degree.
In this embodiment, the system decoding load degree can be calculated by using the formula three.
The formula III is as follows: curLR = (LR 1+ LR2+. + LRnumSe)/numSe
Where, curLR is the system decoding load, numSe is the number of soft decoding sessions (which may include audio decoding sessions or video decoding sessions), LR1 is the decoding load of the first session, LR2 is the decoding load of the second session, and so on.
The process of calculating the session occupancy rate specifically includes:
and recording the number of the current cached data sessions, and calculating the session occupancy rate according to the number of the current cached data sessions and the number of soft decoding sessions. The session occupancy cursertcp may be calculated based on equation three.
The formula III is as follows: cursetpct = number of currently buffered data sessions/number of soft decoding sessions
Wherein, curSePCT is the session occupancy rate, the number of currently cached data sessions is the number of sessions that have started decoding work, and the number of soft decoding sessions is the number of sessions (upper limit value) set in advance.
And S2, adjusting decoding parameters by monitoring the use data of system resources.
The method mainly comprises the step of automatically adjusting the decoding concurrency number or soft decoding conversation number through the acquired use data of the system resources such as the system decoding load degree, the conversation occupancy rate, the CPU occupancy rate and the memory occupancy rate. That is, the usage data of these system resources are compared with the respective corresponding preset thresholds to determine whether the decoding concurrency number or the soft decoding session number needs to be adjusted. The system resources can be used singly or in combination.
In the examples of the present invention, the following cases are listed:
in the first case: system decoding load degree and CPU occupancy rate coordination
And judging whether the decoding load degree of the system is greater than the maximum decoding load degree or not and whether the CPU occupancy rate is less than the CPU allocable threshold value or not. And if the system decoding load degree is greater than the maximum decoding load degree and the CPU occupancy rate is less than the CPU allocable threshold value, increasing the decoding concurrency number.
In the second case: system decoding load degree and soft decoding conversation number
And judging whether the decoding load degree of the system is greater than the maximum decoding load degree or not and whether the soft decoding conversation number is greater than the minimum supporting conversation number or not. If the system decoding load degree is larger than the maximum decoding load degree and the soft decoding conversation number is larger than the minimum supporting conversation number, the soft decoding conversation number is reduced.
In the third case: the system decoding load degree is matched with the conversation occupancy rate and the memory occupancy rate
And judging whether the decoding load degree of the system is smaller than the minimum decoding load degree, whether the session occupancy rate is larger than the maximum session occupancy threshold value or not and whether the memory occupancy rate is smaller than the memory allocable threshold value or not. If the system decoding load degree is smaller than the minimum decoding load degree, the session occupancy rate is larger than the maximum session occupancy threshold value, and the memory occupancy rate is smaller than the memory allocable threshold value, the number of soft solution sessions is increased.
In a fourth case: memory occupancy rate and soft decoding session number
And judging whether the memory occupancy rate is greater than the upper limit threshold of the memory and whether the soft decoding conversation number is greater than the minimum supported conversation number. And if the memory occupancy rate is greater than the memory upper limit threshold value and the number of soft decoding sessions is greater than the minimum number of supported sessions, reducing the number of soft decoding sessions.
In the fifth case: CPU occupancy and decoding concurrency coordination
And judging whether the CPU occupancy rate is greater than the CPU upper limit threshold value or not and whether the decoding concurrency number is greater than the minimum supported decoding concurrency number or not. And if the CPU occupancy rate is greater than the CPU upper limit threshold value and the decoding concurrency number is greater than the minimum supported decoding concurrency number, reducing the decoding concurrency number.
It should be understood that, in addition to the five cases listed above, the description of the embodiment for more cases is omitted. In addition, the above listed cases and the cases not listed above can be realized in a combined manner, for example, whether the first case is satisfied or not is judged first, and if not, whether the second case is satisfied or not can be judged continuously. If the second condition is not satisfied, the third condition may be further determined. The present invention does not limit the judging sequence, for example, it can be judged whether the fifth condition is satisfied first, if not, it can be continuously judged whether the first condition is satisfied.
And S3, decoding the audio and video based on the adjusted decoding parameters.
After the decoding concurrency number or the soft decoding session number is adjusted, the audio and video can be decoded based on the adjusted decoding concurrency number or the adjusted soft decoding session number.
In addition, the number of decoding concurrency or soft decoding sessions may be adjusted based on a preset period. For example, the usage data of the system resources, such as the system decoding load, the session occupancy, the CPU occupancy, and the memory occupancy, may be acquired every 30 minutes, and the decoding concurrency number or the soft decoding session number may be automatically adjusted based on the usage data.
According to the audio and video multi-path concurrent decoding method, the decoding parameters for decoding the audio and video and the use data of the system resources are obtained, the decoding parameters are adjusted by monitoring the use data of the system resources, and the audio and video are decoded based on the adjusted decoding parameters, so that the system hardware resources are fully utilized, the decoding efficiency is improved, and the stable operation of the system is ensured.
The following describes the audio-video multi-channel concurrent decoding method according to the present invention in detail by using a specific embodiment.
Fig. 2 is a flowchart of an audio/video multi-channel concurrent decoding method according to an embodiment of the present invention.
As shown in fig. 2, the method comprises the steps of:
s201, reading a control threshold value from the configuration file.
The invention sets four system load thresholds as the judgment basis for the use of system resources: the CPU may allocate a threshold ReuseCpu, a CPU upper limit threshold MaxCpu, a memory allocable threshold ReuseMem, and a memory upper limit threshold MaxMem. These several thresholds determine the system resource conditions available to the decoding software and can be expressed as a percentage. When the CPU occupancy rate is lower than ReuseCpu or the memory occupancy rate is lower than ReuseMem, the use of corresponding resources (the number of soft decoding sessions and the like) can be increased; when the CPU occupancy is higher than MaxCpu, or the memory occupancy is higher than MaxMem, the use of the corresponding resource may be reduced.
The invention also sets two decoding load degree control thresholds, wherein the minimum decoding load degree MinLR and the maximum decoding load degree MaxLR are set, minLR is less than 1, and MaxLR is greater than 1. In addition, a session occupation threshold MaxSePCT is also set, wherein the MaxSePCT represents the percentage of the number of the currently cached sessions in the number of the sessions supported by the system and can be used as a judgment basis for adjusting the decoding load of the system.
When the decoding software starts to run, system load thresholds ReuseCpu, maxCpu, reuseMem and MaxMem can be read from the configuration file; decoding load degree control thresholds MinLR and MaxLR; the session occupancy threshold MaxSePCT.
And S202, starting decoding according to the default decoding parameters and recording the session parameters.
The default decoding parameters are preset decoding concurrency numbers numDecoders and soft decoding session numbers numSe.
The decoding software may begin decoding according to preset numDecoders and numses. And recording session parameters required for calculating the decoding load degree for each session. The session parameters comprise FPS of the video, audio code rate, decoding duration, total number of decoding output frames and the like.
And S203, calculating the decoding load degree and the session occupancy rate, and acquiring the CPU occupancy rate and the memory occupancy rate.
The process of adjusting the decoding parameters may be initiated periodically (e.g., every 30 minutes). Firstly, reading the current CPU occupancy rate CurCpu and the memory occupancy rate CurMem of the system, calculating the decoding load degree CurLR of the current system, and calculating the session occupancy rate CurSePCT.
Calculating the decoding load degree CurLR of the current system, which is concretely as follows:
the decoding load degree LRi of a certain video session i = Wi video FPS/(the total number of decoded output frames of session i/the decoding time length of session i). Where Wi is a video decoding weighted value of session i, which may be set according to an encoding format and a decoding frame resolution.
The decoding load of a certain audio session j LRj = Wj × bps/(Bj × 8/Tj). Wherein Wj is an audio decoding weighted value, bps is an audio code rate, bj is the number of bytes decoded by the audio, and Tj is the decoding duration.
Since audio decoding consumes much less resources than video decoding, the decoding weight Wj for all audio sessions should be multiplied by a factor m smaller than 1 based on its original value.
After the decoding load LR of each session is calculated, curLR = (LR 1+ LR2+ ·+ LRnumSe)/numSe. The session may include both an audio session and a video session.
Calculating the conversation occupancy rate CurSePCT, which is as follows:
cursetpct = (session amount of currently cached data/supported session upper limit amount). When CurSePCT is larger than MaxSePCT, the current conversation of the system is greatly occupied, and the requirement of increasing the upper limit of the conversation path number exists.
After the decoding load degree and the session occupancy rate are calculated, curCpu can be compared with { ReuseCpu, maxCpu }, curMem can be compared with { ReuseMem, maxMem }, curLR can be compared with { MinLR, maxLR }, and CurSePCT can be compared with MaxSePCT. Thereby adjusting the decoding parameters according to the comparison result.
S204, judging whether the CurMem is larger than MaxMem and the numSe is larger than MinSe.
Where MinSe is the minimum number of sessions that the system can support.
S205, if yes, reducing supportable session upper limit, and returning to step S203.
S206, if not, judging whether the CurCpu is larger than the MaxCpu and the numDecoders are larger than the MinDecoders.
Wherein MinDecoders is the minimum supportable decoding concurrency number of the system.
S207, if yes, reducing the decoding concurrency number, and returning to the step S203.
S208, if not, judging whether the CurLR is larger than MaxLR and whether the CurCpu is smaller than ReuseCpu.
S209, if yes, increasing the decoding concurrency number, and returning to the step S203.
S210, if not, judging whether the CurLR is larger than MaxLR and the numSe is larger than MinSe.
S211, if yes, reducing supportable conversation upper limit, and returning to step S203.
S212, if not, judging whether the CurLR is smaller than MinLR, whether the CurSePCT is larger than MaxSePCT and whether the CurMem is smaller than ReuseMem.
S213, if so, increasing the supportable upper limit of the session.
The invention carries out periodic evaluation on the system decoding load degree, the session occupancy rate and the like by combining the actual application scene of the service aiming at the scene of audio and video multi-path concurrent decoding, can automatically adjust the soft decoding session path number and the decoding concurrency number of decoding software in the decoding process, and ensures that the decoding software can support the maximum soft decoding session path number and the decoding concurrency number in the resource available range. The whole adjustment process is automatically carried out by decoding software in the service operation process, manual intervention is not needed, and the method can be self-adaptive to hardware platforms and data stream scenes with different configurations. And the available range of the resources is user-configurable, and the user can flexibly control the occupation condition of the hardware resources by the decoding software.
In order to implement the above embodiments, the present invention further provides an audio/video multi-channel concurrent decoding apparatus.
Fig. 3 is a schematic structural diagram of an audio/video multi-channel concurrent decoding apparatus according to an embodiment of the present invention.
As shown in fig. 3, the apparatus includes an obtaining module 31, an adjusting module 32, and a decoding module 33.
The obtaining module 31 is configured to obtain decoding parameters for decoding the audio and video and usage data of system resources. Wherein the decoding parameters include: decoding concurrency number and soft decoding conversation number, wherein the system resource comprises at least one of the following: CPU occupancy, memory occupancy, system decoding load and session occupancy.
And an adjusting module 32, configured to adjust the decoding parameter by monitoring the usage data of the system resource.
And the decoding module 33 is configured to decode the audio and video based on the adjusted decoding parameter.
It should be understood that the audio-video multi-channel concurrent decoding apparatus of this embodiment is consistent with the description of the audio-video multi-channel concurrent decoding method of the first aspect embodiment, and is not described herein again.
The audio and video multi-path concurrent decoding device provided by the embodiment of the invention can be used for fully utilizing the hardware resources of the system, improving the decoding efficiency and ensuring the stable operation of the system by acquiring the decoding parameters for decoding the audio and video and the use data of the system resources, monitoring the use data of the system resources, adjusting the decoding parameters and decoding the audio and video based on the adjusted decoding parameters.
In order to implement the above embodiments, the present invention further provides a computer device.
The computer device comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, and when the processor executes the computer program, the audio and video multi-path concurrent decoding method as the embodiment of the first aspect is realized.
In order to implement the above embodiments, the present invention also provides a non-transitory computer-readable storage medium.
The non-transitory computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the audio and video multi-path concurrent decoding method as an embodiment of the first aspect.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It should be noted that in the description of the present specification, reference to the description of the term "one embodiment", "some embodiments", "an example", "a specific example", or "some examples", etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
In the description of the specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Claims (22)

1. An audio and video multi-path concurrent decoding method is characterized by comprising the following steps:
acquiring decoding parameters and use data of system resources for decoding audio and video, wherein the decoding parameters comprise: decoding concurrency number and soft decoding session number, wherein the system resource comprises at least one of the following: CPU occupancy rate, memory occupancy rate, system decoding load degree and session occupancy rate;
adjusting the decoding parameters by monitoring usage data of the system resources;
decoding the audio and video based on the adjusted decoding parameters;
wherein adjusting the decoding parameters by monitoring usage data of the system resources comprises:
judging whether the system decoding load degree is greater than the maximum decoding load degree or not, and whether the CPU occupancy rate is less than a CPU distributable threshold or not;
and if the system decoding load degree is greater than the maximum decoding load degree and the CPU occupancy rate is less than the CPU assignable threshold value, increasing the decoding concurrency number.
2. The method of claim 1, wherein the system decoding load is calculated based on a decoding load of each session.
3. The method of claim 1, wherein the session occupancy is calculated based on a number of current buffered data sessions and the number of soft decoding sessions.
4. The method of claim 2, wherein calculating the system decoding load based on the decoding load of each session comprises:
calculating the decoding load degree of each path of session;
and calculating the average value of the decoding load degrees of all the conversations based on the decoding load degree of each conversation, and taking the average value as the system decoding load degree.
5. The method of claim 4, wherein when the current session is video decoding, calculating the decoding load of each session comprises:
acquiring a video refresh rate (FPS), decoding duration, the number of decoding output frames and a video decoding weighted value of a current session, wherein the video decoding weighted value is in direct proportion to video resolution and video coding complexity;
and calculating the decoding load degree of the current session according to the video refresh rate FPS, the decoding duration, the decoding output frame quantity and the video decoding weighted value.
6. The method according to claim 4, wherein when the current session is audio decoding, calculating the decoding load degree of each session, comprises:
acquiring the audio code rate, the decoding duration, the audio decoding byte number and the audio decoding weighted value of the current session;
and calculating the decoding load degree of the current conversation according to the audio code rate, the decoding duration, the audio decoding byte number and the audio decoding weighted value, wherein the audio decoding weighted value is in direct proportion to the audio coding complexity.
7. The method of claim 1, wherein adjusting the decoding parameters by monitoring usage data of the system resources comprises:
judging whether the decoding load degree of the system is greater than the maximum decoding load degree or not, and whether the soft decoding conversation number is greater than the minimum supporting conversation number or not;
and if the system decoding load degree is greater than the maximum decoding load degree and the soft decoding conversation number is greater than the minimum supporting conversation number, reducing the soft decoding conversation number.
8. The method of claim 1, wherein adjusting the decoding parameters by monitoring usage data of the system resources comprises:
judging whether the system decoding load degree is smaller than the minimum decoding load degree, whether the session occupancy rate is larger than the maximum session occupancy threshold value, and whether the memory occupancy rate is smaller than the memory allocable threshold value;
and if the system decoding load degree is smaller than the minimum decoding load degree, the session occupancy rate is larger than the maximum session occupancy threshold value, and the memory occupancy rate is smaller than the memory allocable threshold value, increasing the number of the soft decoding sessions.
9. The method of claim 1, wherein adjusting the decoding parameters by monitoring usage data of the system resources comprises:
judging whether the memory occupancy rate is greater than a memory upper limit threshold value or not, and whether the soft decoding conversation number is greater than the minimum support conversation number or not;
and if the memory occupancy rate is greater than the memory upper limit threshold value and the soft decoding conversation number is greater than the minimum supported conversation number, reducing the soft decoding conversation number.
10. The method of claim 1, wherein adjusting the decoding parameters by monitoring usage data of the system resources comprises:
judging whether the CPU occupancy rate is greater than a CPU upper limit threshold value or not, and whether the decoding concurrency number is greater than a minimum supported decoding concurrency number or not;
and if the CPU occupancy rate is greater than the CPU upper limit threshold value and the decoding concurrency number is greater than the minimum supported decoding concurrency number, reducing the decoding concurrency number.
11. An audio-video multi-path concurrent decoding apparatus, comprising:
an obtaining module, configured to obtain a decoding parameter for decoding an audio/video and usage data of a system resource, where the decoding parameter includes: decoding concurrency number and soft decoding session number, wherein the system resource comprises at least one of the following: CPU occupancy rate, memory occupancy rate, system decoding load degree and session occupancy rate;
the adjusting module is used for adjusting the decoding parameters by monitoring the use data of the system resources;
the decoding module is used for decoding the audio and video based on the adjusted decoding parameters;
the adjusting module is configured to:
judging whether the system decoding load degree is greater than the maximum decoding load degree or not, and whether the CPU occupancy rate is less than a CPU distributable threshold or not;
and if the system decoding load degree is greater than the maximum decoding load degree and the CPU occupancy rate is less than the CPU assignable threshold value, increasing the decoding concurrency number.
12. The apparatus of claim 11, wherein the acquisition module is to:
and calculating the system decoding load degree based on the decoding load degree of each session.
13. The apparatus of claim 11, wherein the acquisition module is to:
and calculating the session occupancy rate based on the current cached data session number and the soft decoding session number.
14. The apparatus of claim 12, wherein the acquisition module is to:
calculating the decoding load degree of each path of session;
and calculating the average value of the decoding load degrees of all the conversations based on the decoding load degree of each conversation, and taking the average value as the system decoding load degree.
15. The apparatus of claim 14, wherein the obtaining module is specifically configured to:
when the current session is video decoding, acquiring a video refresh rate (FPS), a decoding duration, a decoding output frame number and a video decoding weighted value of the current session, wherein the video decoding weighted value is in direct proportion to a video resolution and a video coding complexity;
and calculating the decoding load degree of the current session according to the video refresh rate FPS, the decoding duration, the decoding output frame quantity and the video decoding weighted value.
16. The apparatus of claim 14, wherein the acquisition module is specifically configured to:
when the current conversation is audio decoding, acquiring the audio code rate, the decoding duration, the audio decoding byte number and the audio decoding weighted value of the current conversation;
and calculating the decoding load degree of the current conversation according to the audio code rate, the decoding duration, the audio decoding byte number and the audio decoding weighted value, wherein the audio decoding weighted value is in direct proportion to the audio coding complexity.
17. The apparatus of claim 11, wherein the adjustment module is to:
judging whether the decoding load degree of the system is greater than the maximum decoding load degree or not, and whether the soft decoding conversation number is greater than the minimum supporting conversation number or not;
and if the system decoding load degree is greater than the maximum decoding load degree and the soft decoding conversation number is greater than the minimum supporting conversation number, reducing the soft decoding conversation number.
18. The apparatus of claim 11, wherein the adjustment module is to:
judging whether the system decoding load degree is smaller than the minimum decoding load degree, whether the session occupancy rate is larger than the maximum session occupancy threshold value or not, and whether the memory occupancy rate is smaller than the memory distributable threshold value or not;
and if the system decoding load degree is smaller than the minimum decoding load degree, the session occupancy rate is larger than the maximum session occupancy threshold value, and the memory occupancy rate is smaller than the memory allocable threshold value, increasing the number of the soft decoding sessions.
19. The apparatus of claim 11, wherein the adjustment module is to:
judging whether the memory occupancy rate is greater than a memory upper limit threshold value or not, and whether the soft decoding conversation number is greater than the minimum support conversation number or not;
and if the memory occupancy rate is greater than the memory upper limit threshold value and the soft decoding conversation number is greater than the minimum supporting conversation number, reducing the soft decoding conversation number.
20. The apparatus of claim 11, wherein the adjustment module is to:
judging whether the CPU occupancy rate is greater than a CPU upper limit threshold value or not, and whether the decoding concurrency number is greater than a minimum supported decoding concurrency number or not;
and if the CPU occupancy rate is greater than the CPU upper limit threshold value and the decoding concurrency number is greater than the minimum supported decoding concurrency number, reducing the decoding concurrency number.
21. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the audio/video multi-channel concurrent decoding method according to any one of claims 1 to 10 when executing the computer program.
22. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the audio-visual multi-channel concurrent decoding method according to any one of claims 1 to 10.
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