Streaming media transmission optimization method and system

Abstract

The invention discloses a streaming media transmission optimization method and system, and the method comprises the following steps: S1, receiving streaming media data, carrying out the statistics of a streaming media code rate, and carrying out the network bandwidth prediction through a Kalman filtering model; S2, carrying out code rate estimation and prediction in a non-packet-loss input mode by using a streaming media code rate and a network bandwidth prediction result; S3, performing bidirectional predictive code rate verification on the round-trip time, the packet loss rate and the code rate estimation predicted value to obtain a sending end output code rate; and S4, according to the given code rate, outputting a corresponding streaming media code rate to carry out flow control. Network condition prediction suitable for wired and wireless scenes is provided, real-time and rapid prediction of the current network rate is met, smooth transition of the code rate is guaranteed, streaming media playing is smoother, TCP friendliness is guaranteed in combination with the TFRC algorithm, meanwhile, the video code rate under low bandwidth is guaranteed, the situation that the video is stuck and paused due to the fact that the prediction rate is too low is reduced. The method is a congestion control solution based on streaming media transmission.

Description

technical field [0001] The invention is applied in the field of streaming media transmission, and specifically relates to a streaming media transmission optimization method and system. Background technique [0002] The current mainstream streaming media communication solution to network congestion: one is based on the RTCP control protocol, which provides feedback on the network round-trip time and packet loss rate, so that the reflection of the network situation is relatively one-sided, and the sender is only responsible for ensuring its own bandwidth. The friendliness of TCP also aggravates network congestion; the other is to imitate the principle of TCP flow control and apply it to UDP transmission. Issues such as bandwidth misprediction. In the current field, the packet loss rate and round-trip time are generally used for bandwidth prediction, and the packet loss rate is not accurate for wireless network conditions. The main problem of congestion control based on AIMD ...

AI Technical Summary

Problems solved by technology

[0002] The current mainstream streaming media communication solutions to deal with network congestion: one is based on the RTCP control protocol, which gives feedback on the network round-trip time and packet loss rate, so that the reflection of the network situation is relatively one-sided, and the sender is only responsible for ensuring its own bandwidth. The friendliness of TCP also aggravates network congestion; the other is to imitate the principle of TCP flow control and apply it to UDP transmission. Issues such as bandwidth misprediction

In the current field, the packet loss rate and round-trip time are generally used for bandwidth prediction, and the packet loss rate is not accurate for wireless network conditions.

The main problem of congestion control based on AIMD algorithm is that the range of rate adjustment is relatively large, which is prone to suddenness and jitter, which is not conducive to the stability of streaming media; the main problem of congestion control based on TFRC algorithm is that when congestion occurs When the rate of the receiving end is too low, causing problems such as video freeze and incoherent playback, the second is that when connecting wirelessly, the bandwidth is wrongly estimated and the bit rate is reduced.

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