  In disaster scenarios, conventional terrestrial multi-access edge computing
(MEC) paradigms, which rely on fixed infrastructure, may become unavailable due
to infrastructure damage. With high-probability line-of-sight (LoS)
communication, flexible mobility, and low cost, unmanned aerial vehicle
(UAV)-assisted MEC is emerging as a new 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 planning optimization
problem (JTRTOP) to maximize the QoE of UDs while considering the energy
consumption 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 planning 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 provide
empirical evidence supporting the superior system performance of the proposed
OJTRTA in comparison to alternative approaches.
