Streaming media code rate adaptive method, device and equipment supporting neural network

Abstract

The invention relates to a streaming media code rate self-adaption method and device supporting a neural network and computer equipment. The method comprises the following steps: acquiring a historical network throughput measurement value, a preset vector of available resolution and current buffer occupation information, inputting the historical network throughput measurement value, the preset vector of available resolution and the current buffer occupation information into a pre-constructed Bayesian neural network, outputting a throughput prediction value of a next time period, constructing a model prediction control optimization model by taking preset QoE index optimization as a target, and solving to obtain a predicted downloading bit rate of the current video block; after execution, obtaining a corresponding reward value according to the QoE index, continuously training the Bayesian neural network according to the predicted downloading bit rate and the reward value, and adaptively obtaining the optimal bit rate of the downloaded video block according to the continuously trained Bayesian neural network and the model prediction control optimization model. The throughput prediction accuracy and the mobile network video quality are improved.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a neural network-supported stream media code rate adaptive method, device and computer equipment. Background technique [0002] According to Cisco VNI, by 2022, video traffic will account for more than 80% of all IP traffic, and video applications will become an application with an absolute advantage in traffic. While video traffic continues to grow, users also demand better video quality. In recent years, many technologies that can improve user experience quality have emerged, but the core of these technologies is the ABR algorithm, which dynamically determines the quality level of each segment. The ABR algorithm needs to strike a balance between high quality, minimal rebuffering and less quality switching to maximize the overall QoE. [0003] For relatively long-term video streaming, how the ABR algorithm predicts future network and client conditions has a signifi...

AI Technical Summary

Problems solved by technology

However, since the neural network scheme requires more resources, such prediction has a lag and usually cannot correctly reflect the bit rate at this time, so the ABR algorithm may not be able to obtain the most balanced QoE

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