UAV-Assisted Edge Computing and Streaming for Wireless Virtual Reality: Analysis, Algorithm Design, and Performance Guarantees

被引:48
作者
Zhang, Liang [1 ,2 ]
Chakareski, Jacob [3 ]
机构
[1] New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA
[2] George Mason Univ, Dept Elect & Comp Engn, Fairfax, VA 22030 USA
[3] Ying Wu Coll Comp, Newark, NJ 07102 USA
关键词
Streaming media; Quality of experience; Relays; Autonomous aerial vehicles; Virtual reality; Resource management; Servers; Unmanned aerial vehicles (UAV); mobile edge computing (MEC); Internet of Things (IoT); virtual reality; 360-degree video; joint resource allocation; wireless 360-degree video streaming; VEHICLE BASE STATION; 3-D PLACEMENT; NETWORKS; RADIO;
D O I
10.1109/TVT.2022.3142169
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Emerging virtual reality (VR) applications require high data rate transmission and low end-to-end latency, which has become one of the main challenges for future wireless networks. Unmanned aerial vehicle (UAV) mounted base stations and computing facilities can be used to provide better wireless connectivity and computing services to edge VR users to meet their computing needs and reduce the end-to-end latency. We propose a novel UAV assisted mobile edge computing (MEC) network to enable high-quality mobile 360-degree video VR applications by leveraging UAVs to provide the required communication and computing needs. Then, we formulate the joint UAV placement, MEC and radio resource allocation, and 360-degree video content layer assignment (UAV-MV) problem, which aims to select the allocation of computing and communications resources and the location of the UAVs such that the delivered quality of experience (QoE) is maximized across the mobile VR users, given various system constraints. We show that the problem is NP-hard, and decompose it into three lower-complexity subproblems that we solve sequentially. We design an approximation algorithm with performance guarantees that solves the UAV-MV problem based on the solutions to the three subproblems. Our simulation results show that the average QoE enabled by the proposed algorithm is 15% and 90% greater relative to two competitive reference methods.
引用
收藏
页码:3267 / 3275
页数:9
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