Cooperative Caching, Rendering, and Beamforming for RIS-Assisted Wireless Virtual Reality Networks

Wireless virtual reality networks(WVRNs) provide seamless connectivity between virtual reality devices with colossal application and commercial value. However, the main problem restricting its development is the high energy and computational consumption in 3D video rendering on VR devices. To address this issue, we propose a novel coordinated multi-point (CoMP) and reconfigurable intelligent surfaces (RISs) assisted system, where the video is rendered by multiple collaborative mobile edge computing (MEC) servers simultaneously. Besides, BSs associated with these MEC servers are formed as a CoMP cluster to achieve a high data rate. This paper aims to minimize long-term power consumption by jointly optimizing the video caching and rendering at the MEC servers and the beamforming for both BSs and RIS. We propose an online, hybrid learning framework that combines deep reinforcement learning (DRL) for video caching and rendering, and an alternating optimization for the beamforming of all BSs and the RIS. In particular, the reward of each action in the DRL algorithm is calculated by the proposed alternating optimization problem, thus reducing the action space and accelerating convergence speed. Numerical results and comparison experiments show that our proposed method can effectively reduce the long-term average power consumption of the system, satisfy the requirement of 3D video transmission with low computational complexity, and outperform that without CoMP and RIS techniques.