A Decoupled Learning Strategy for MEC-enabled Wireless Virtual Reality (VR) Network

Wireless-connected Virtual Reality (VR) provides immersive experience for VR users from anywhere at anytime. However, providing wireless VR users with seamless connectivity and real-time VR video with high quality is challenging due to its requirements in high Quality of Experience (QoE) and low VR interaction latency under limited computation capability of VR device. To address these issues, we propose a Mobile Edge Computing (MEC)-enabled wireless VR network, where the field of view (FoV) of each VR user can be real-time predicted using Recurrent Neural Network (RNN), and the rendering of VR content is moved from VR device to MEC server with rendering model migration capability. Taking into account the geographical and FoV request correlation, we propose Deep Reinforcement Learning (DRL) strategies to maximize the long-term QoE of VR users under VR interaction latency constraint. Simulation results show that our proposed MEC rendering schemes and DRL algorithms substantially improve the long-term QoE of VR users and reduce the VR interaction latency compared to MEC rendering with nearest association scheme.