Flow control and flow control model training method and device for multimedia data

Abstract

The present application relates to a method and device for performing flow control on multimedia data and training a flow control model. Including: combining the communication status data and the current encoding data in the current cycle to obtain the combined data to be processed; performing self-attention mechanism processing on the combined data to be processed to obtain intermediate processing features, and performing multi-task classification based on the intermediate processing features, Outputting target flow control data of at least one dimension; based on the target encoding data determined by the target flow control data, triggering flow control processing on multimedia data generated in a next period during the multimedia communication process. Since the importance of different types of encoded data and communication status data can be distinguished through the self-attention mechanism, the role of primary and secondary derivation of high-level characterization features can be fully utilized, so that the output target flow control data can be used in the flow of the next cycle. It is more adaptable to the complex and changeable real network environment during control processing, and the flow control effect is good.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to a method and device for performing flow control on multimedia data and training a flow control model. Background technique [0002] With the development of network technology, more and more multimedia data transmission scenarios require real-time monitoring of network status, so as to realize flow control of multimedia data transmission according to the monitored network status. Taking the multimedia data transmission scenario as an example of a voice or video real-time call scenario, the voice or video real-time call is usually implemented by using a network RTC (Real-Time Communication, real-time call). In an RTC call, it is usually necessary to monitor the network status in real time, and then modify the overall call configuration according to the real-time network status. For example, if the network status is good, the encoding rate can be increased...

AI Technical Summary

Problems solved by technology

[0003] In the traditional solution, the GCC algorithm in WebRtc is widely used in the current adaptive bit rate control algorithm, which is the network congestion control algorithm for real-time media communication. However, when the GCC algorithm is actually used, there will be a certain delay, and Excessive reliance on empirical configuration leads to inaccurate flow control in actual control

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