A Deep Reinforcement Learning-Based Optimal Computation Offloading Scheme for VR Video Transmission in Mobile Edge Networks

Abstract

Large bandwidth, Low latency and intensive computing are the main challenge in high-performance virtual reality (VR) video transmission. As mobile edge computing (MEC) can provide computation and storage resources closer to terminals, it has been a promising mode in VR video transmission to substantially improve communication quality. This work focuses on the autonomous perception ability in MEC-supported VR video transmission, and introduces deep reinforcement learning to investigate optimal task offloading solutions. Therefore, this paper proposes a deep reinforcement learning-based optimal computation offloading scheme for VR video transmission in mobile edge networks. Specifically, a Deep Deterministic Policy Gradient-based computation offloading algorithm in designed as the main technical framework. The optimal planning of computation offloading strategies is viewed as a Markov decision problem, and a deep Q-Network is employed to deal with it. Finally, the setting of MEC-supported VR video transmission scenes is simulated, in which the proposed scheme is implemented for evaluation. The results are displayed in visualization format and show that the proposed task computation scheme can possess proper performance results in MEC-supported VR video transmission scenes.

Keywords

• Virtual reality
• video transmission
• computation offloading
• mobile edge networks
• reinforcement learning