Real-time video code rate self-adaption method based on mobile edge calculation

Abstract

The invention provides a real-time video code rate self-adaptation method based on mobile edge calculation. The method comprises the following steps: S1, acquiring parameters such as video playing, network connection and equipment performance of a user in real time; S2, selecting a task scheduling strategy with the optimal performance for the user according to the parameters in S1; S3, automatically switching working modes according to the selected task scheduling strategy; S4, calculating the QoE of the current scheduling strategy according to the parameters such as video playing condition inthe next iteration period, the equipment energy consumption and the like, and feeding back the QoE to the edge server; S5, by the edge server, updating a scheduling strategy state transition matrix according to the feedback QoE; and S6, repeating the above steps, and continuously performing iterating and updating, so that the performance of the final edge scheduling strategy tends to be optimal.According to the invention, on the basis of relatively low cost, relatively high energy consumption can be reduced for mobile multimedia equipment, and continuous growth and optimization of a complexvideo code rate adaptive algorithm learning system are helped.

Description

technical field [0001] The invention belongs to the field of edge computing, in particular to a real-time video code rate adaptive method based on mobile edge computing. Background technique [0002] Due to the rapid development of smart mobile devices and the rise of various video APPs, more and more users start to watch videos through mobile devices. At present, most video service providers hope to provide high-quality video services to as many users as possible, thereby increasing the viewing time of users. However, mobile smart devices are always accompanied by more complex network fluctuations. Therefore, some video bit rate adaptive algorithms based on network conditions have emerged at the historic moment. Appropriate bit rate to avoid freezing and improve video service quality. [0003] Most of the current mainstream video bit rate algorithms concentrate computing tasks on the cloud server side, which causes mobile devices to consume extra energy in the process of ...

AI Technical Summary

Problems solved by technology

However, MEC servers have limited computing power and poor scalability

With the explosive growth of mobile users and emerging applications, relying solely on MEC cannot fully meet the computing offloading needs of mobile users

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