Online delay optimization for MEC and RIS-assisted wireless VR networks

• Published in: Wireless networks 2024-05, Vol.30 (4), p.2939-2959
• Main Authors: Jia, Jie, Yang, Leyou, Chen, Jian, Ma, Lidao, Wang, Xingwei
• Format: Article
• Language: English
• Subjects: Algorithms; Communications Engineering; Computer Communication Networks; Edge computing; Electrical Engineering; Engineering; IT in Business; Markov processes; Mobile computing; Multiagent systems; Network latency; Networks; Optimization; Original Paper; Power control; Reconfigurable intelligent surfaces; Rendering; Servers; User experience; Virtual networks; Virtual reality; Wireless networks
• ISSN: 1022-0038; 1572-8196
• DOI: 10.1007/s11276-024-03706-4

As wireless networks continue to advance, virtual reality (VR) transmission over wireless connections is progressively transitioning from concept to practical application. Although this technology can significantly enhance the VR user experience, its development bottleneck lies in the computing capacity of devices and transmission latency. Considering the limited computational resources of VR devices for rendering tasks, multi-access edge computing (MEC) servers are introduced to provide powerful computing capabilities. To cope with transmission latency, reconfigurable intelligent surface (RIS) enhances links between base stations (BSs) and users. Based on these two technologies, we propose a RIS-assisted VR streaming model, where BSs are equipped with MEC servers to assist data rendering. Firstly, the user association, power control, and RIS phase shift optimization problems in the VR transmission system are jointly modeled and analyzed, establishing a long-term minimization of the interaction delay model. Secondly, by modeling the optimization problem as a Markov decision process (MDP), a joint optimization framework based on multi-agent deep reinforcement learning (MADRL) is proposed. In this framework, we have separately designed two dedicated algorithms for discrete and continuous variables. Furthermore, multiple agents can provide feedback based on user experience and cooperate with each other to improve the joint strategy. Finally, the performance and superiority of the proposed solution and algorithm are validated through simulation experiments in different application scenarios.