Joint Optimization of Resource Allocation and FOV for VR services in Mobile Edge Networks
Wenhao Song, Ying Wang, Man Liu, Zixuan Fei · 2020
In order to meet the high-speed and low-latency requirements of virtual reality (VR) services, field of view (FOV) is widely considered. This paper makes efficient use of Mobile Edge Computing (MEC) to extract different FOV videos from 360-degree videos, which can avoid the transmission bandwidth usage and backhaul link traffic between base station and core network. Problems of cache placement and FOV selection are studied for wireless VR services networks. The joint content caching and FOV selection is formulated as an optimization problem whose purpose is to maximize the utility function of the system under the existing network resources. A Joint Optimization of Resource Allocation and FOV algorithm is proposed to solve the multi-variable coupled non-convex problem using Taylor expansion and successive convex optimization. Simulation results show that the proposed algorithm satisfies latency deadlines and maximizes system revenue with the high efficiency used resources.