Latency Optimization of UAV-Enabled MEC System for Virtual Reality Applications Under Rician Fading Channels

Abstract

This letter considers an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system, where UAV base stations (UBSs) are deployed to cache, process, and deliver virtual reality (VR) content from a cloud server to VR users (VRUs). Under Rician fading channel model, we optimize various resource allocation parameters, e.g., association of VRUs with UBSs, caching policy, computing-capacity allocation, and location of UBSs, with the objective of minimizing the maximum latency, subject to computing, caching, and power constraints at the UBSs. The problem is non-convex and solved by using alternating optimization and successive convex approximation techniques. Our simulation results clearly show the importance of the proposed joint optimization algorithm, in terms of performance improvement, over different benchmark schemes.