CN113347114A - Real-time streaming media transmission control method and device facing deadline sensing - Google Patents
Real-time streaming media transmission control method and device facing deadline sensing Download PDFInfo
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- CN113347114A CN113347114A CN202110619476.3A CN202110619476A CN113347114A CN 113347114 A CN113347114 A CN 113347114A CN 202110619476 A CN202110619476 A CN 202110619476A CN 113347114 A CN113347114 A CN 113347114A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/24—Traffic characterised by specific attributes, e.g. priority or QoS
- H04L47/2416—Real-time traffic
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/12—Avoiding congestion; Recovering from congestion
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/25—Flow control; Congestion control with rate being modified by the source upon detecting a change of network conditions
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/32—Flow control; Congestion control by discarding or delaying data units, e.g. packets or frames
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/50—Queue scheduling
- H04L47/62—Queue scheduling characterised by scheduling criteria
- H04L47/625—Queue scheduling characterised by scheduling criteria for service slots or service orders
- H04L47/6275—Queue scheduling characterised by scheduling criteria for service slots or service orders based on priority
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Abstract
The invention discloses a transmission control method and a device of real-time streaming media facing to deadline sensing, wherein the method comprises the following steps: calculating the remaining cut-off time of a data frame to be sent; calculating the weight of each data frame according to the residual deadline; selecting a data frame corresponding to the minimum weight in the weights to perform preferential transmission; if the data frame is about to miss the requirement of the deadline, judging whether the current data frame needs to be added with a redundant data packet, if so, setting a redundancy rate through the decision of a deep reinforcement learning model, adding the redundant data packet, sending the current data frame according to the decided sending rate, and if not, sending the current data frame according to the current sending rate; and transmitting the data frame at a new data frame transmission rate by acquiring the deep reinforcement learning model. The method reasonably adjusts the frame scheduling, the redundancy rate and the sending rate of a sending end under the requirement of giving priority and cut-off time to the real-time streaming media application, and maximizes the QoE of the real-time streaming media application.
Description
Technical Field
The present invention relates to the field of streaming media technologies, and in particular, to a transmission control method and apparatus for real-time streaming media oriented to deadline sensing.
Background
In recent years, real-time streaming media applications such as live or video conferencing, different data frames have different priority and deadline constraints in order to meet the user experience. For example, in a cloud game or an online conference, the priority of the control signal is highest, and the delay requirement does not exceed 50 ms; the one-way delay requirement of the audio stream is not more than 100ms while defined as medium priority; and the video stream has the lowest priority, and the time delay does not exceed 500 ms. There is a universal deadline requirement in real-time video streaming.
Many scholars have proposed various transmission control methods for video streaming, such as packet loss-based methods (Cubic, Reno, etc.) and model-based methods (BBR, GCC, etc.), which will achieve as high a bit rate as possible when network conditions allow. But if the data frames miss the deadline by the time they arrive at the recipient, they are not submitted to the upper layer application. In this case, even if a high throughput is achieved, a large amount of bandwidth is actually wasted. Yet another approach to providing low latency transmission, i.e., delay-based rate control (Vegas, LEDBAT, Copa, etc.) schemes, is to transmit data based on the reliable protocol TCP or QUIC. In addition, some converged control schemes, such as WebRTC, provide a real-time communication service that combines video codec, Forward Error Correction (FEC), and GCC congestion control to optimize video data transmission. Salisify combines a codec and a transmission process to determine the coding rate based on the available bandwidth. Although these above solutions provide low latency transmission, they do not guarantee that the data arrives at the receiver before the deadline. The latest draft by IETF proposes unreliable transport protocols based on QUIC in an attempt to meet the latency requirements of applications. However, these schemes still use the default congestion control scheme in QUIC, Reno, a hardened congestion control algorithm to achieve lower throughput in high packet loss networks. The current transmission control methods cannot effectively sense different deadline of a data frame and effectively adjust a sending strategy of a sending end.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, an object of the present invention is to provide a deadline-aware-oriented transmission control method for real-time streaming media, which effectively reduces bandwidth waste, improves a completion rate of a high-priority data frame before the deadline, and improves user experience through reasonable data frame scheduling, redundancy policy, and sending rate.
Another objective of the present invention is to provide a transmission control device for real-time streaming media oriented to deadline awareness.
To achieve the above object, an embodiment of the present invention provides a method for controlling transmission of real-time streaming media facing deadline awareness, including:
calculating the remaining cut-off time of a data frame to be sent;
calculating the weight of each data frame according to the residual deadline;
selecting the data frame corresponding to the minimum weight in the weights to perform preferential transmission;
if the data frame is about to miss the requirement of the deadline, judging whether the current data frame needs to be added with a redundant data packet, if so, setting a redundancy rate through the decision of a deep reinforcement learning model, adding the redundant data packet, sending the current data frame according to the decided sending rate, and if not, sending the current data frame according to the current sending rate;
and transmitting the data frame at a new data frame transmission rate by acquiring the deep reinforcement learning model.
The deadline sensing-oriented transmission control method for the real-time streaming media can adapt to the requirements of real-time streaming media application with various deadlines and priorities and make adaptive adjustment on frame scheduling, redundancy strategies and sending rate of a sending end under diversified network states, thereby reducing the waste of various bandwidths and effectively improving user experience.
In addition, the transmission control method for real-time streaming media facing deadline awareness according to the above embodiment of the present invention may further have the following additional technical features:
optionally, the method further comprises:
if the data frame has missed the deadline, the data frame and the related data frames depending on the data frame are discarded.
Optionally, the method further comprises:
after the data frame is sent, the optimal sending rate suitable for the current network condition is calculated through a reinforcement learning technology according to the measurement of the network state and the feedback of the data frame completion rate.
Optionally, the calculating the remaining deadline time of the data frame to be transmitted includes:
the dead line is the deadline of each data frame, the past _ time is the time that the current data frame has waited at the sending end, the remaining _ size is the remaining size of the current data frame, the affected _ rtt is the currently measured round-trip delay, and the current _ sending _ date is the current sending rate.
Optionally, the calculating the weight of each data frame according to the remaining deadline includes:
wherein priority is the priority of the current data frame, and max _ priority is the value of the lowest priority.
Optionally, the method further comprises:
and if the weights of the data frames are the same, selecting the data frame with the small residual byte number to preferentially transmit.
In order to achieve the above object, another embodiment of the present invention provides a deadline-aware-oriented transmission control apparatus for real-time streaming media, including:
the first calculation module is used for calculating the remaining deadline of a data frame to be sent;
the second calculation module is used for calculating the weight of each data frame according to the residual deadline;
the selection module is used for selecting the data frame corresponding to the minimum weight in the weights to be sent preferentially;
the redundancy module is used for judging whether a current data frame needs to be added with a redundancy data packet or not if the data frame is about to miss the requirement of the deadline, setting a redundancy rate through a decision of a deep reinforcement learning model if the data frame is needed, adding the redundancy data packet, sending the current data frame according to the decided sending rate, and sending the current data frame according to the current sending rate if the data frame is not needed;
and the updating module is used for acquiring a new data frame sending rate and sending the data frame through the deep reinforcement learning model.
According to the deadline-aware-oriented transmission control device for real-time streaming media, disclosed by the embodiment of the invention, the transmission control of a data sending end in a video conference or live scene is reasonably adjusted by applying the real-time streaming media, and the deadline and the constraint conditions of the priority of different data frames are considered, so that the completion rate of the data frames at the deadline of the sending end is maximized, the user experience is improved, and the transmission of real-time streaming media data streams is completed.
In addition, the transmission control device for real-time streaming media facing deadline awareness according to the above embodiment of the present invention may further have the following additional technical features:
optionally, the method further comprises: and the discarding module is used for discarding the data frame and the related data frame depending on the data frame if the data frame has missed the deadline.
Optionally, the method further comprises: a feedback module for
After the data frame is sent, the optimal sending rate suitable for the current network condition is calculated through a reinforcement learning technology according to the measurement of the network state and the feedback of the data frame completion rate.
Optionally, the selection module is further used for
And if the weights of the data frames are the same, selecting the data frame with the small residual byte number to preferentially transmit.
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 foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a method for controlling transmission of real-time streaming media oriented to deadline awareness according to an embodiment of the present invention;
FIG. 2 is a diagram of a deadline aware-oriented real-time streaming media delivery control architecture according to an embodiment of the present invention;
FIG. 3 is a flow diagram of the transmission of an adaptive data frame according to one embodiment of the present invention;
fig. 4 is a schematic structural diagram of a transmission control apparatus for real-time streaming media oriented to deadline awareness according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The following describes a transmission control method and apparatus for real-time streaming media oriented to deadline awareness according to an embodiment of the present invention with reference to the accompanying drawings.
First, a transmission control method for real-time streaming media oriented to deadline awareness according to an embodiment of the present invention will be described with reference to the accompanying drawings.
Fig. 1 is a method for controlling transmission of real-time streaming media oriented to deadline awareness according to an embodiment of the present invention.
As shown in fig. 1, the method for controlling transmission of real-time streaming media facing deadline awareness includes the following steps:
step S1, calculating the remaining deadline of the data frame to be transmitted.
Alternatively, each time a data frame to be transmitted is selected, the scheduler first calculates a remaining _ time for each frame to be transmitted:
where dead is the deadline of each frame, past _ time is the time that the current frame has been waiting at the sending end, remaining _ size is the remaining size of the current frame, affected _ rtt is the currently measured round trip delay, and currentsending _ rate is the current sending rate provided by the congestion control module.
In step S2, a weight for each data frame is calculated based on the remaining deadline.
Optionally, the scheduler calculates weight of each frame according to remaining _ time
The priority is the priority of the current frame, and the smaller the numerical value is, the higher the priority is; max _ priority is the lowest priority value.
In step S3, the data frame corresponding to the smallest weight among the weights is selected and preferentially transmitted.
Optionally, the scheduler selects the data frame with the smallest weight. If the weights of more than 2 frames are the same at the same time, the data frame with smaller remaining _ number of bytes (data frame with smaller remaining number of bytes) is selected. To avoid bandwidth waste, the scheduler actively discards expired frames and frames that rely on their decoding.
Step S4, if the data frame is about to miss the deadline requirement, it is determined whether the current data frame needs to add the redundant data packet, if necessary, the redundancy rate is set through the decision of the deep reinforcement learning model, the redundant data packet is added, and the current data frame is sent according to the decided sending rate, if not, the current data frame is sent according to the current sending rate.
Optionally, if the time slice does not arrive, determining whether redundancy is to be added to the current frame, and if the redundancy is not to be added, transmitting the data packet at the last transmission rate.
If the time slice does not arrive, but the current data frame needs to add a certain FEC redundancy packet to avoid missing the deadline requirement, the redundancy rate is set according to the decision of the deep reinforcement learning model to form an FEC packet, and the data packet is sent according to the sending rate of the new decision.
In step S5, the data frame is transmitted at the new data frame transmission rate obtained by the deep reinforcement learning model.
As shown in fig. 2, the embodiment of the present invention is based on a two-part technical method, in which a deadline-aware scheduler is first used to select the most urgent data frame transmission by comprehensively considering the remaining deadline of the data frame, the priority, the size of the remaining transmission packets, and the currently measured network status, and the data frame that has missed the deadline is discarded together with the data frame that depends on it. And secondly, a deep reinforcement learning model is used, and the redundancy and the sending rate are adaptively determined according to the current network state. And the sending rate of the deep reinforcement learning model decision is provided for a scheduler with deadline perception at the same time, so that the scheduler can select the data frame which is most suitable for the current network condition for transmission.
According to the method and the device, the deadline requirement of upper-layer application can be fully utilized, the data frame to be sent is effectively scheduled, and meanwhile, the self-adaptive decision redundancy rate and sending rate avoid the transmission of overdue frames or undecodable frames, so that the bandwidth waste is reduced. In the process of real-time streaming media transmission, a transmission control strategy can be timely and reasonably adjusted according to variable network states, and the QoE of a user is improved by maximizing the completion rate of high-priority data frames completed before the deadline time.
As shown in fig. 3, the execution flow is 1) when a plurality of data frames with different deadline and priority are to be sent in a buffer queue at a sending end, a heuristic algorithm is used to select the most urgent data frame to be sent first, and meanwhile, the data frame which has missed the deadline and the data frame which depends on the data frame are discarded, so as to avoid waste of bandwidth caused by the transmission of expired frames and frames which cannot be decoded; 2) according to the conditions of the current bandwidth, the link delay and the packet loss rate, based on the reinforcement learning technology, adding a proper redundant packet to a frame which cannot meet the deadline through retransmission, and avoiding bandwidth waste caused by transmitting a data frame which cannot be recovered; 3) according to the measurement of the network state and the feedback of the data frame completion rate, the optimal sending rate suitable for the current network condition is calculated through a reinforcement learning technology, and the waste of bandwidth caused by an unreasonable congestion control mechanism is avoided. On the premise that the deadline and the priority of a given data frame are applied to the real-time streaming media, the frame scheduling of a sending end, the redundancy rate of a data packet and the sending rate can be reasonably adjusted by using a reinforcement learning technology, and the QoE of the real-time streaming media transmission is maximized.
According to the deadline-aware-oriented transmission control method for real-time streaming media, provided by the embodiment of the invention, transmission control of a data sending end in a video conference or live scene is reasonably adjusted by applying the real-time streaming media, and the deadline and the constraint conditions of priorities of different data frames are considered, so that the completion rate of the data frames at the deadline of the sending end is maximized, the user experience is improved, and the transmission of real-time streaming media data streams is completed.
Next, a transmission control apparatus for real-time streaming media oriented to deadline awareness according to an embodiment of the present invention will be described with reference to the accompanying drawings.
Fig. 4 is a schematic structural diagram of a transmission control apparatus for real-time streaming media oriented to deadline awareness according to an embodiment of the present invention.
As shown in fig. 4, the apparatus for controlling transmission of real-time streaming media facing deadline awareness includes: a first calculation module 401, a second calculation module 402, a selection module 403, a redundancy module 404 and an update module 405.
The first calculating module 401 is configured to calculate a remaining deadline of a data frame to be sent.
A second calculating module 402, configured to calculate a weight of each data frame according to the remaining deadline.
A selecting module 403, configured to select a data frame corresponding to the smallest weight in the weights to perform preferential transmission.
A redundancy module 404, configured to determine whether a redundant data packet is to be added to a current data frame if the data frame is about to miss the deadline requirement, set a redundancy rate through a decision of a deep reinforcement learning model if necessary, add the redundant data packet, and send the current data frame according to the decided sending rate, and if not, send the current data frame according to the current sending rate.
And an updating module 405, configured to send the data frame at the new data frame sending rate obtained by the deep reinforcement learning model.
Optionally, in some embodiments, the method further comprises: and the discarding module is used for discarding the data frame and the related data frame depending on the data frame if the data frame has missed the deadline.
Optionally, in some embodiments, the method further comprises: a feedback module for
After the data frame is sent, the optimal sending rate suitable for the current network condition is calculated through a reinforcement learning technology according to the measurement of the network state and the feedback of the data frame completion rate.
Optionally, in some embodiments, the selection module is further configured to
And if the weights of the data frames are the same, selecting the data frame with the small residual byte number to preferentially transmit.
It should be noted that the foregoing explanation of the method embodiment is also applicable to the apparatus of this embodiment, and is not repeated herein.
According to the deadline sensing-oriented real-time streaming media transmission control device provided by the embodiment of the invention, the frame scheduling, the redundancy strategy and the sending rate can be timely and adaptively adjusted reasonably according to diversified deadline requirements and changeable network states, and the three parts work cooperatively to meet the deadline requirements of application as much as possible. The frame scheduling algorithm makes full use of the network state obtained by diversified measurement information and comprehensively considers the deadline requirement of the data frame to obtain the scheduling of the most urgent data frame at present. And dynamically adjusting the redundancy rate and the sending rate by using the information of the current frame output by the scheduling method and the measured network state through a deep reinforcement learning model.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean 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.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A transmission control method for real-time streaming media facing to deadline awareness is characterized by comprising the following steps:
calculating the remaining cut-off time of a data frame to be sent;
calculating the weight of each data frame according to the residual deadline;
selecting the data frame corresponding to the minimum weight in the weights to perform preferential transmission;
if the data frame is about to miss the requirement of the deadline, judging whether the current data frame needs to be added with a redundant data packet, if so, setting a redundancy rate through the decision of a deep reinforcement learning model, adding the redundant data packet, sending the current data frame according to the decided sending rate, and if not, sending the current data frame according to the current sending rate;
and transmitting the data frame at a new data frame transmission rate by acquiring the deep reinforcement learning model.
2. The method of claim 1, further comprising:
if the data frame has missed the deadline, the data frame and the related data frames depending on the data frame are discarded.
3. The method of claim 1, further comprising:
after the data frame is sent, the optimal sending rate suitable for the current network condition is calculated through a reinforcement learning technology according to the measurement of the network state and the feedback of the data frame completion rate.
4. The method of claim 1, wherein calculating the remaining deadline time for a frame of data to be transmitted comprises:
where dead is the deadline of each data frame, past _ time is the time that the current data frame has been waiting at the sending end, remaining _ size is the remaining size of the current data frame, affected _ rtt is the currently measured round-trip delay, and current _ sending _ rate is the current sending rate.
5. The method of claim 1, wherein said calculating a weight for each data frame based on the remaining deadline comprises:
wherein priority is the priority of the current data frame, and max _ priority is the value of the lowest priority.
6. The method of claim 1, further comprising:
and if the weights of the data frames are the same, selecting the data frame with the small residual byte number to preferentially transmit.
7. An apparatus for controlling transmission of real-time streaming media facing deadline awareness, comprising:
the first calculation module is used for calculating the remaining deadline of a data frame to be sent;
the second calculation module is used for calculating the weight of each data frame according to the residual deadline;
the selection module is used for selecting the data frame corresponding to the minimum weight in the weights to be sent preferentially;
the redundancy module is used for judging whether a current data frame needs to be added with a redundancy data packet or not if the data frame is about to miss the requirement of the deadline, setting a redundancy rate through a decision of a deep reinforcement learning model if the data frame is needed, adding the redundancy data packet, sending the current data frame according to the decided sending rate, and sending the current data frame according to the current sending rate if the data frame is not needed;
and the updating module is used for acquiring a new data frame sending rate and sending the data frame through the deep reinforcement learning model.
8. The apparatus of claim 7, further comprising: and the discarding module is used for discarding the data frame and the related data frame depending on the data frame if the data frame has missed the deadline.
9. The apparatus of claim 7, further comprising: a feedback module for
After the data frame is sent, the optimal sending rate suitable for the current network condition is calculated through a reinforcement learning technology according to the measurement of the network state and the feedback of the data frame completion rate.
10. The apparatus of claim 7, wherein the selection module is further configured to select the selected cell from a group consisting of a cell, a cell
And if the weights of the data frames are the same, selecting the data frame with the small residual byte number to preferentially transmit.
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