Content-Aware Proactive VR Video Caching for Cache-Enabled AP over Edge Networks

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With the rapid development of virtual reality (VR) video networked applications, the use of network caching mechanisms to guarantee the quality of VR services has been proven to be a very effective method. Most of the existing methods on cache placement prediction only consider the one-sided information of user viewpoints and do not consider the video characteristic information of virtual reality, because the asymmetry of the two types of information causes the accuracy of current predictions to gradually decrease, which affects the cache hit rate and leads to VR performance metrics that cannot be guaranteed. In this paper, we analyze the demanding requirements of VR for low latency and high bandwidth in a multi-access point (multi-AP) scenario environment, and further improve the cache hit rate of user requests by increasing network throughput. First, the throughput of VR users after associating APs is analyzed using a Markov model. Second, a nonlinear mixed integer programming problem is constructed with the goal of maximizing the overall throughput of the network system. Finally, combining the characteristics of the VR video content itself and the popularity of the requested video content, the symmetry of the information is guaranteed by considering the ratio between the video characteristic information and the user feature information to determine the weights. The experimental results demonstrate that the proposed algorithm achieves the improvement of cache hit rate and the improvement of network throughput while ensuring the quality of service.