  Given flexible mobility, rapid deployment, and low cost, unmanned aerial
vehicle (UAV)-enabled mobile edge computing (MEC) shows great potential to
compensate for the lack of terrestrial edge computing coverage. However,
limited battery capacity, computing and spectrum resources also pose serious
challenges for UAV-enabled MEC, which shorten the service time of UAVs and
degrade the quality of experience (QoE) of user devices (UDs) {\color{b}
without effective control approach}. In this work, we consider a UAV-enabled
MEC scenario where a UAV serves as an aerial edge server to provide computing
services for multiple ground UDs. Then, a joint task offloading, resource
allocation, and UAV trajectory planning optimization problem (JTRTOP) is
formulated to maximize the QoE of UDs under the UAV energy consumption
constraint. To solve the JTRTOP that is proved to be a future-dependent and
NP-hard problem, an online joint optimization approach (OJOA) is proposed.
Specifically, the JTRTOP is first transformed into a per-slot real-time
optimization problem (PROP) by 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 validate that the proposed
approach can achieve superior system performance compared to the other
benchmark schemes.
