Abstract
Resource allocation in base stations (BSs) and computation capacity at multi-access edge computing (MEC) servers interact each other and together determine the quality of experience (QoE) of emerging applications. In this paper, we aim at optimizing the QoE for video services in air-ground integrated MEC networks, which integrate millimeter wave (mmWave) small-cell base stations (SBSs) and unmanned aerial vehicle (UAVs) equipped with MEC servers. Specifically, a novel QoE maximization problem based on the latency satisfaction is formulated as a mixed-integer nonlinear problem (MINLP) that jointly optimize user equipment (UE) association, video version selection and computation resource allocation. Due to the challenging combinatorial nature of the problem, the decomposition method is employed to decouple the original problem into three tractable subproblems, which can be solved efficiently by using three-sided matching and standard optimization solvers. Simulation results demonstrate that our proposed joint algorithm achieves higher QoE compared with the benchmark methods.