QoE Maximization for Multiple-UAV-Assisted Multi-Access Edge Computing via an Online Joint Optimization Approach

TL;DR

This paper proposes an online joint optimization approach for UAV-assisted multi-access edge computing to maximize user QoE in disaster scenarios, addressing resource constraints and dynamic network conditions.

Contribution

It introduces a hierarchical UAV-MEC architecture and a novel online optimization method that outperforms existing algorithms in maximizing user QoE.

Findings

At least 10% QoE improvement over DRL-based algorithms

Effective real-time joint task offloading and UAV trajectory control

Validated superiority through extensive simulations

Abstract

In disaster scenarios, conventional terrestrial multi-access edge computing (MEC) paradigms, which rely on ground infrastructure, may become unavailable due to infrastructure damage. With high-probability line-of-sight (LoS) communication, flexible mobility, and low cost, uncrewed aerial vehicle (UAV)-assisted MEC is emerging as a promising paradigm to provide edge computing services for ground user devices (UDs) in disaster-stricken areas. However, the limited battery capacity, computing resources, and spectrum resources also pose serious challenges for UAV-assisted MEC, which can potentially shorten the service time of UAVs and degrade the quality of experience (QoE) of UDs without an effective control approach. To this end, in this work, we first present a hierarchical architecture of multiple-UAV-assisted MEC networks that enables the coordinated provision of edge computing services by multiple UAVs. Then, we formulate a joint task offloading, resource allocation, and UAV trajectory control optimization problem (JTRTOP) to maximize the QoE of UDs while considering the energy and resource constraints of UAVs. Since the problem is proven to be a future-dependent and NP-hard problem, we propose a novel online joint task offloading, resource allocation, and UAV trajectory control approach (OJTRTA) to solve the problem. Specifically, the JTRTOP is first transformed into a per-slot real-time optimization problem (PROP) using the Lyapunov optimization framework. Then, a two-stage optimization method based on game theory and convex optimization is proposed to solve the PROP. Simulation results show that the proposed OJTRTA outperforms various benchmark approaches and achieves at least a 10% improvement in the QoE of UDs compared to deep reinforcement learning (DRL)-based algorithms, thereby validating the superiority of the proposed approach.