CN114630183A - Edge device caching method and evaluation method based on scalable coding - Google Patents
Edge device caching method and evaluation method based on scalable coding Download PDFInfo
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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
- H04N21/47—End-user applications
- H04N21/472—End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification, for manipulating displayed content
- H04N21/47202—End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification, for manipulating displayed content for requesting content on demand, e.g. video on demand
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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
- H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream, rendering scenes according to MPEG-4 scene graphs
- H04N21/44004—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream, rendering scenes according to MPEG-4 scene graphs involving video buffer management, e.g. video decoder buffer or video display buffer
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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
- H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream, rendering scenes according to MPEG-4 scene graphs
- H04N21/4402—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream, rendering scenes according to MPEG-4 scene graphs involving reformatting operations of video signals for household redistribution, storage or real-time display
- H04N21/440281—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream, rendering scenes according to MPEG-4 scene graphs involving reformatting operations of video signals for household redistribution, storage or real-time display by altering the temporal resolution, e.g. by frame skipping
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- Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
Abstract
The invention relates to a scalable coding-based edge device caching method and an evaluation method. The algorithm comprises the following steps: collecting video request information in an area received by an edge device; classifying the collected video request information according to the request time, the request video number and the request video code rate; counting the number of requests of each code rate of each video of a plurality of past time slices including a specified time slice when the time slice is ended; calculating the cache cost performance of each video code rate according to the number of the requests; and adjusting the video content cached by the device in the next time slice according to the size sequence of the caching performance prices. The invention also provides a method for quantifying the amplitude score provided by the edge device for the user by the content cached in the edge device in a time period. The algorithm and the evaluation method thereof utilize the advantages of SVC scalable coding on caching, can effectively improve the caching efficiency of a single device, and improve the capability of providing video service for users by utilizing an edge caching technology.
Description
Technical Field
The invention belongs to the technical field of computer networks, and particularly relates to a scalable coding-based edge device caching method and an assessment method.
Background
With the increasing network video streaming services, the video streaming services are becoming the hottest services on the internet. According to the predictions of cisco, the consumed traffic of the video streaming service will reach 82% of all network traffic by the end of 2021. However, efficient delivery of video content remains a challenging task due to network heterogeneity in terms of resolution, frame rate, and bit depth among users with different preferences and limitations.
Two important criteria for video on demand quality of service are video response latency and bandwidth usage. The video response time delay directly influences the experience of the user when watching the video, and the shorter the video response time delay is, the more comfortable experience can be obtained when the user watches the video, thereby improving the quality of the server of the video on demand service. The occupied bandwidth of the video affects network resources consumed in video transmission, if the occupied bandwidth of the video is too large, network congestion may be caused when a large number of video-on-demand requests occur in an area, the time delay and the stability of video transmission are affected, and the watching experience of all video-on-demand users in the area is reduced.
In order to provide high quality video on demand services, video providers are currently always using content delivery networks, CDNs, to provide video on demand. The CDN adds a new network architecture to an existing network, delivers and transmits content from a source station to an edge area closest to a user, so that the user can access desired content nearby, thereby improving the response speed of user access. The basic principle of the CDN is that a part of content required by a user is deployed to a place closest to the user by means of cache servers placed in various places through function modules such as global scheduling and content distribution, time delay generated by physical distance when the user carries out video on demand service is reduced, an originally low-efficiency and unreliable network is converted into a high-efficiency and reliable intelligent network, the higher requirement of the user on content access quality is met, the problem of internet network congestion is solved, and the response speed of the user for accessing a website is increased. Furthermore, the performance of employing video content caching devices at the edge of the network is comparable to that employed at intermediate nodes of the network.
However, with the popularization of emerging short video platforms and the increase of the video service demand of users, the video traffic in the internet has an explosive increase, and the traditional CDN technology is increasingly difficult to meet the increasingly stringent video service demand of users, and at this time, an edge cache concept is proposed. In the traditional network, contents (videos, web pages and the like) are deployed in a data center or a regional CDN cache server, so that the problems of content acquisition end-to-end delay time, limited return bandwidth, low-efficiency redundant transmission and the like exist, and the requirements of low delay and the like of 5G and future applications cannot be met. The core idea of the mobile edge cache is to sink the content to a network access unit (such as a 5G base station, a roadbed unit, etc.), so as to realize one-hop type nearby content service, significantly reduce the end-to-end delay, and improve the network transmission efficiency.
Disclosure of Invention
Due to the limited capacity of the edge buffer, it is a problem how to provide high-quality video service for the vod users by utilizing the limited buffer space to the maximum extent.
In order to solve the above problems, the present invention discloses a scalable coding-based edge device caching method, SVC-BEC (SVC based on edge caching),
the method comprises the following steps:
s1, collecting the video request information in the area received by the device on an edge device;
s2, classifying the collected video request information according to request time, request video number and request video code rate;
s3, when a specified time slice is finished, counting the number of requests of each video code rate of a plurality of past time slices including the time slice;
s4, calculating the cache cost performance of each video code rate according to the number of requests;
and S5, adjusting the video content cached by the device in the next time slice according to the size sequence of the caching cost performance.
The invention further prefers: wherein in the step S1, the following steps are specifically included:
s11, selecting a normally running edge cache device and determining the size of the cache space of the edge cache device;
s12, counting the video buffer service provided by the device, and designing the data structure for collecting the request information;
and S13, acquiring data in the actual environment and recording the request time.
The invention further prefers: wherein in the step S2, the following steps are specifically included:
s21, classifying the collected information according to the request time, the request video number and the request video code rate;
s22, classifying the collected data into a data structure according to the previous design;
and S23, storing the classified data information into the equipment.
The invention further prefers: in step S3, the method specifically includes the following steps:
s31, designing the time slice length, and pausing the video request response when one time slice is finished;
s32, counting the number of requests of each video code rate of a plurality of past time slices including the time slice in the past;
and S33, classifying the counted data according to the video type and the code rate.
The invention further prefers: in step S4, the method specifically includes the following steps:
s41, according to the time limitation, the video request quantity can approximately represent the video heat;
s42, extracting the request quantity of m time slices past each code rate of each video in the file and summing;
s43, dividing the space occupied by the cached video by the heat to obtain the caching cost performance of the video in the past m time slices;
and S45, storing the caching cost performance of each video code rate into a file.
The invention further prefers: the S41 specifically includes:
s411, calling a file storing video request information;
s412, checking whether each video receives a request from a user in the past m time periods;
s43, if a bitrate of a video is requested in the past m time slices, it needs to be counted.
The invention further prefers: step S5 specifically includes the following steps:
s51, ranking the caching cost performance of all videos requested in the past from high to low;
s52, storing the video with the highest caching performance-price ratio into the current edge caching equipment;
s53, in the process of putting, if the current video i is found to be put into the equipment, if the code rate of the stored video i is higher than the code rate of the current processing, skipping the version of the current code rate, and if the stored code rate is lower than the code rate of the current processing video i, covering the corresponding code rate of the current processing video i with the previously stored video i;
s54, the process is repeated until the space occupied by the video stored by device D reaches the upper storage limit.
The invention relates to a scalable coding-based edge device cache quality evaluation method, which specifically comprises the following steps:
s6, because the peak signal-to-noise ratio PSNR is in direct proportion to the video code rate, representing the peak signal-to-noise ratio by using the logarithm of the video code rate, and approximately representing the experience score obtained by the user from the video;
s7, calculating the sum of the user experience scores provided by all videos cached in the equipment in the past time period;
and S8, dividing the sum by the number of user requests in the past time period for standardization, and obtaining the user experience score S of the caching scheme of the device in the past time period.
The invention further prefers: the step S7 specifically includes the following steps:
s71, for a video code rate, multiplying the user request number by the PSNR value, namely the logarithm of the code rate to obtain a video score S;
s72, counting the sum of scores S of all kinds of video code rates;
and S73, counting the sum S of scores of requested videos in the past time slice.
The invention further prefers: in step S8, if S is larger, it indicates that the quality of the video service provided by the device to the user in the past time slice is higher; if S is smaller, the system indicates that the quality of the video service provided by the equipment for the user in the past time slice is lower
The invention has the advantages that aiming at the video on demand service, the buffer quality of the edge equipment can be effectively improved by utilizing the advantages of the scalable coding in the aspect of storage based on the time limitation principle, and the evaluation can be carried out through the user score.
Drawings
FIG. 1 is a flow chart that schematically illustrates a statistical method provided by an embodiment of the present invention;
fig. 2 is a diagram schematically illustrating a video request information collection structure provided by an embodiment of the present invention;
fig. 3 schematically shows a cache content replacement flowchart provided in an embodiment of the present invention.
Detailed Description
The present invention will be further illustrated with reference to the accompanying drawings and specific embodiments, which are to be understood as merely illustrative of the invention and not as limiting the scope of the invention. It should be noted that the terms "front," "back," "left," "right," "upper" and "lower" used in the following description refer to directions in the drawings, and the terms "inner" and "outer" refer to directions toward and away from, respectively, the geometric center of a particular component.
As shown in fig. 1, an embodiment of the present invention provides a scalable coding based edge device caching method, including:
and S1, collecting the video request information in the area received by the device on an edge device. Fig. 2 schematically shows a structure diagram of information acquisition, and referring to fig. 2, a detailed description is provided below.
The specific implementation content is as follows:
firstly, selecting edge equipment, recording the size D of a cache space of the edge equipment, deploying probes on the edge equipment to acquire user request information, acquiring by using a plurality of network cards on hardware, and shunting original request flow to each network card by a switch through a load balancing technology to meet the information acquisition requirement.
Then, a double-buffer mechanism is applied to the network card buffer area, and the effective sequence of the buffer area is controlled through the time slice, so that the network card can be ensured to be in an available state all the time. When a certain network card buffer zone is full, the working thread can transfer the buffer zone address to the reentrant data collection interface.
And finally, in order to collect the request data of all the network cards in parallel, a multithreading technology is adopted. A DPDK suite is deployed on the acquisition server, and the rapid data packet processing function of the DPDK suite can be used for information acquisition after being adjusted; and the acquisition server transmits the video request information of the user into the storage area.
S2, classifying the collected video request information according to request time, request video number and request video code rate;
the specific implementation content is as follows: extracting the collected video request information from the storage area; classifying the collected information according to request time, request video number and request video code rate; counting the total times of requesting video content in each time slice; the collected data is sorted into data structures according to previous designs.
And S3, when a specified time slice is finished, counting the heat of each code rate of each video of a plurality of past time slices including the time slice. The specific implementation steps are as follows:
firstly, for a code rate j of a video i, extracting the times C of the video i being requested by a user in the past m time slicesij。
Then, in order to eliminate the difference between the time slice with the large number of requests and the time slice with the small number of requests, the extracted number of requests for a specific video rate is divided by the total number of requests per time slice Rt, thereby performing normalization.
Finally, adding the m normalized data to obtain the heat of the video code rate in the past m time slices, namely
The above operation is repeated for all video rates requested in the past m time slices.
S4, calculating the cache cost performance of each video code rate according to the number of requests;
due to the time limitation principle, the popularity of videos with high popularity in the past is high in the future, that is, more user requests are received, but due to the fact that the caching space of the device is limited, if the videos are cached too much, the total number of videos capable of being cached is reduced, and the overall request hit rate of the device is reduced, in order to improve the overall caching quality of the caching device, the caching cost performance of each video code rate is calculated, and the cached videos are selected according to the cost performance.
For the past m time slices, locating all video code rate heat information files in the time slice; reading the file, and extracting the heat value C of all video code rates in the fileij' dividing the heat value by the code rate v of the video versionijRepresenting the space occupied by the video, thereby obtaining the cache cost performance W of the video versionij。
And S5, adjusting the video content cached by the device in the next time slice according to the size sequence of the caching performance price.
For all rate versions of all videos, W is addedijSorting from big to small, storing the video I into the edge device from the first bit, in the process of the placement, if the current video I is found to be placed into the device, if the code rate of the stored video I is higher than the code rate of the current processing, skipping the version of the current code rate, if the stored code rate is lower than the code rate of the current processing video I, covering the corresponding code rate of the current processing video i with the previously stored video I, and repeating the process until the space occupied by the video stored in the device D reaches the storage upper limit M, wherein the specific example of the process is shown in FIG. 3
The invention also provides a cache quality evaluation method, which comprises the following steps:
s6 PSNR due to peak signal-to-noise ratio and videoThe code rate is in direct proportion, the logarithm of the video code rate is used for representing the peak signal-to-noise ratio, and approximately representing the experience score obtained by a user from the video, namely S is consideredij＝PSNRij＝lgvijRepresenting the experience score obtained by the user from the hit of the code rate j version of the request video i;
and S7, calculating the sum of the user experience scores provided by all the videos cached in the equipment in the past time period. For a video code rate, multiplying the number of user requests by the PSNR value, namely the logarithm of the code rate to obtain a video score s; counting the sum of scores s of all kinds of video code rates; counting the sum S of scores of requested videos in a past time slice;
s8, dividing the sum by the number of user requests in the past time period for normalization, i.e.
Thereby obtaining a caching scheme user experience score S for the device over a past time period. If S is larger, the higher the quality of the video service provided by the equipment for the user in the past time slice is; if S is smaller, it is indicated that the quality of the video service provided by the device to the user in the past time slice is lower.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features.
Claims (10)
1. A scalable coding-based edge device caching method is characterized in that: the method comprises the following steps:
s1, collecting the video request information in the area received by the device on an edge device;
s2, classifying the collected video request information according to request time, request video number and request video code rate;
s3, when a specified time slice is finished, counting the number of requests of each video code rate of a plurality of past time slices including the time slice;
s4, calculating the cache cost performance of each video code rate according to the number of requests;
and S5, according to the sequence of the cost performance of the cache, adjusting the video content cached by the device in the next time slice.
2. The scalable coding based edge device caching method according to claim 1, wherein:
wherein in the step S1, the following steps are specifically included:
s11, selecting a normally running edge cache device and determining the size of the cache space of the edge cache device;
s12, counting the video buffer service provided by the device, and designing the data structure for collecting the request information;
and S13, acquiring data in the actual environment and recording the request time.
3. The scalable coding based edge device caching method according to claim 1, wherein:
wherein in the step S2, the following steps are specifically included:
s21, classifying the collected information according to the request time, the request video number and the request video code rate;
s22, classifying the collected data into a data structure according to the previous design;
and S23, storing the classified data information into the equipment.
4. A scalable coding based edge device caching method according to claim 1, wherein: in step S3, the method specifically includes the following steps:
s31, designing the time slice length, and pausing the video request response when one time slice is finished;
s32, counting the number of requests of each video code rate of a plurality of past time slices including the time slice in the past;
and S33, classifying the counted data according to the video type and the code rate.
5. A scalable coding based edge device caching method according to claim 1, wherein: in step S4, the method specifically includes the following steps:
s41, according to the time limitation, the video request quantity can approximately represent the video heat;
s42, extracting the request quantity of m time slices past each code rate of each video in the file and summing;
s43, dividing the space occupied by the cached video by the heat to obtain the caching cost performance of the video in the past m time slices;
and S45, storing the caching cost performance of each code rate of each video into a file.
6. A scalable coding based edge device caching method as claimed in claim 4, characterized in that: the S41 specifically includes:
s411, calling a file storing video request information;
s412, checking whether each video receives a request from a user in the past m time periods;
s43, if a bitrate of a video is requested in the past m time slices, it is necessary to count its popularity.
7. The scalable coding based edge device caching method according to claim 1, wherein the step S5 specifically includes the following steps:
s51, ranking the caching cost performance of all videos requested in the past from high to low;
s52, storing the video with the highest caching performance-price ratio into the current edge caching equipment;
s53, in the process of putting, if the current video i is found to be put into the device, if the stored code rate of the video i is higher than the current processing code rate, skipping the current code rate version, if the stored code rate is lower than the current processing code rate of the video i, covering the corresponding code rate of the current processing video i with the previously stored video i;
s54, the process is repeated until the space occupied by the video stored by device D reaches the upper storage limit.
8. The scalable coding based edge device buffer quality assessment method according to claim 1, wherein: the method specifically comprises the following steps:
s6, because the peak signal-to-noise ratio PSNR is in direct proportion to the video code rate, representing the peak signal-to-noise ratio by using the logarithm of the video code rate, and approximately representing the experience score obtained by the user from the video;
s7, calculating the sum of the user experience scores provided by all videos cached in the equipment in the past time period;
and S8, dividing the sum by the number of user requests in the past time period for standardization, and obtaining the user experience score S of the caching scheme of the device in the past time period.
9. The scalable coding-based edge device cache assessment method according to claim 8, wherein: the step S7 specifically includes the following steps:
s71, for a video code rate, multiplying the user request number by the PSNR value, namely the logarithm of the code rate to obtain a video score S;
s72, counting the sum of scores S of all kinds of video code rates;
and S73, counting the sum S of scores of requested videos in the past time slice.
10. The scalable coding-based edge device cache assessment method according to claim 8, wherein: in step S8, if S is larger, it indicates that the quality of the video service provided by the device to the user in the past time slice is higher; if S is smaller, it is indicated that the quality of the video service provided by the device to the user in the past time slice is lower.
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