Joint Rendering Offloading and Resource Allocation Scheme for MEC-Assisted RS VR Systems

With the increasing demand of virtual reality (VR) applications, wireless systems need to provide ultra-high data rate to support VR streaming for multiple users simultaneously. In this paper, we propose a mobile edge computing-assisted rate splitting (RS) VR streaming transmission scheme to pursue better quality of experience (QoE) and alleviate the computing burden of VR users (VUs). We formulate an optimization problem to minimize the weighted energy consumption while ensuring the required QoE. The quantization parameters selection, rendering offloading decision, transmit precoding, rate allocation, and computing resource allocation are optimized and a joint Wrendering offloading and resource allocation algorithm is proposed. Simulation results validate the efficiency of the proposed algorithm and reveal the performance gain obtained from RS.