VR video multistage caching method and system based on reinforcement learning in C-RAN architecture

Abstract

The invention discloses a VR video multistage caching method and system based on reinforcement learning in a CRAN architecture. The method comprises the steps of collecting network throughput, user request information and caching state information; determining the user experience quality of the single user based on the video quality, the video time domain jitter, the video space domain jitter andthe time delay; determining the user experience quality of multiple users based on the user experience quality of the single user; optimizing the user experience quality of multiple users based on a reinforcement learning algorithm; and caching the VR video based on the network throughput, the user request information, the caching state information and the optimized user experience quality of themultiple users. According to the method and the device, the time delay can be reduced by reasonably deploying the multi-level cache in the C-RAN, and repeated data propagation is reduced, so that morecomfortable VR video watching experience is provided for a user.

Description

technical field [0001] The present invention relates to the technical field of computer networks, in particular to a VR (Virtual Reality, virtual reality) video multi-level caching method and system based on reinforcement learning in a C-RAN (Cloud Radio Access Network, cloud radio access network) architecture. Background technique [0002] With the development of VR technology, VR has gradually entered thousands of households, providing users with immersive video viewing experience, and also playing an indispensable role in the fields of educational interaction, industrial remote guidance, and telemedicine. According to statistics, as of 2019, the number of VR users in China has exceeded 10 million, and the industry revenue of virtual reality software and hardware has exceeded 1 billion yuan. It is foreseeable that virtual reality technology will flourish in the future and will expand to more In the field of application, it can provide users with a more realistic, high-defi...

AI Technical Summary

Problems solved by technology

For example, transmitting 8K panoramic VR video requires a bandwidth of more than 260Mbps, and if you want to obtain a more extreme experience effect, you need a bandwidth of more than 10Gbps, which is a huge challenge for the current network, especially the backbone network of the video source server. challenge

On the other hand, users are very sensitive to the delay of VR videos. Generally speaking, if the delay exceeds 20ms, they will have a strong sense of dizziness, which is disastrous for the viewing experience of VR videos.

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