A Unified Resource Allocation Framework for Virtual Reality Streaming over Wireless Networks

Although Rate Splitting Multiple Access (RSMA) is a promising scheme to effectively manage interference and enhance data rate and spectral utilization, its applications for Virtual Reality (VR) streaming have not been well studied. In addition to the strict latency requirement as in conventional High-Definition streaming, VR streaming further requires more computing resources at the transmitter to promptly react to the dynamic of users' Field-of-View interests. Unfortunately, current conventional RSMA approaches could not effectively handle these problems since they are not intentionally developed to deal with the special features of VR streaming. To address these challenges, we first propose a novel hierarchical multicast technique to effectively integrate the RSMA and VR streaming by exploiting the Field-of-Views of VR users. Then, the VR streaming problem established based on RSMA is formulated as a joint computation and communication optimization problem which can not only guarantee VR streaming latency requirement but also effectively manage interferences among users. Finally, due to the dynamic and uncertainty of wireless channels and users' demands, we develop a deep reinforcement learning approach to find the optimal policy for the system. This learning solution allows us to find the optimal parameters for the system via trail-and-error learning process, and thus it is effective in dealing with the uncertainty and unknown information from surrounding environment. Simulation results demonstrate that our proposed solution can satisfy the VR requirement of millisecond latency that is much lower than those of the baselines.