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An Online Joint Optimization Approach for QoE Maximization in UAV-Enabled Mobile Edge Computing

Long He, Geng Sun, Zemin Sun, Pengfei Wang, Jiahui Li, Shuang Liang, Dusit Niyato

TL;DR

This work tackles the problem of maximizing user QoE in UAV-enabled MEC under UAV energy constraints by formulating a future-dependent, NP-hard joint optimization problem (JTRTOP) for task offloading, resource allocation, and UAV trajectory. It introduces an online solution (OJOA) that first converts JTRTOP into a per-slot real-time problem via Lyapunov optimization, then solves it with a two-stage approach: convex-resource-aware offloading and a game-theoretic offloading decision (MU-TOG) followed by convexified UAV trajectory planning using SCA. Theoretical analysis guarantees UAV energy constraint satisfaction and a suboptimality bound, while simulations show OJOA outperforms several baselines in time-average UD cost and manages UAV workload and energy effectively. The results demonstrate a practical, scalable framework for QoE-centric, energy-aware UAV MEC in dynamic environments.

Abstract

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.

An Online Joint Optimization Approach for QoE Maximization in UAV-Enabled Mobile Edge Computing

TL;DR

This work tackles the problem of maximizing user QoE in UAV-enabled MEC under UAV energy constraints by formulating a future-dependent, NP-hard joint optimization problem (JTRTOP) for task offloading, resource allocation, and UAV trajectory. It introduces an online solution (OJOA) that first converts JTRTOP into a per-slot real-time problem via Lyapunov optimization, then solves it with a two-stage approach: convex-resource-aware offloading and a game-theoretic offloading decision (MU-TOG) followed by convexified UAV trajectory planning using SCA. Theoretical analysis guarantees UAV energy constraint satisfaction and a suboptimality bound, while simulations show OJOA outperforms several baselines in time-average UD cost and manages UAV workload and energy effectively. The results demonstrate a practical, scalable framework for QoE-centric, energy-aware UAV MEC in dynamic environments.

Abstract

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.
Paper Structure (19 sections, 11 theorems, 46 equations, 3 figures, 3 algorithms)

This paper contains 19 sections, 11 theorems, 46 equations, 3 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Model and Problem Formulation
  4. Basic Model
  5. Communication Model
  6. Computation Model
  7. Cost Model
  8. Problem Formulation
  9. Lyapunov-Based Problem Transformation
  10. Two-Stage Optimization Algorithm
  11. Stage 1: Task Offloading and Resource Allocation
  12. Resource Allocation
  13. Task Offloading
  14. Stage 2: UAV Trajectory Planning
  15. Main Steps of OJOA and Performance Analysis
  16. ...and 4 more sections

Key Result

Theorem 1

For all $t$ and all possible queue backlogs $\mathbf{Q}_u(t)$, the drift-plus-penalty is upper bounded as where $W=\frac{1}{2} \max \left\{\left(\bar{E_u^{\mathrm{c}}}\right)^2,\left(E_{\max }^{\mathrm{c}}-\bar{E_u^{\mathrm{c}}}\right)^2\right\}+\frac{1}{2} \max \left\{\left(\bar{E_u^{\mathrm{p}}}\right)^2,\left(E_{\max }^{\mathrm{p}}-\bar{E_u^{\mathrm{p}}}\right)^2\right\}$ is a finite constant.

Figures (3)

  • Figure 1: The UAV-enabled MEC consists a UAV and multiple ground UDs. The UAV provides computing services to UDs by allocating communication and computing resources. Each UD independently decides to compute its task locally or offload the task to the UAV.
  • Figure 2: System performance with respect to the time slots. (a) Time-average UD cost. (b) Time-average UAV energy consumption. (c) Time-average UAV workload.
  • Figure 3: System performance with respect to the task data size. (a) Time-average UD cost. (b) Time-average UAV energy consumption. (c) Time-average UAV workload.

Theorems & Definitions (25)

  • Theorem 1
  • proof
  • Lemma 1
  • proof
  • Theorem 2
  • proof
  • Definition 1
  • Definition 2
  • Definition 3
  • Theorem 3
  • ...and 15 more