Joint Optimization of Delay and Energy Efficiency for UAV Task Offloading and Cooperative Scheduling

Abstract

The growing demand for multimedia services in Internet of Things (IoT) networks has significantly increased the traffic load on backhaul links, making Mobile Edge Caching (MEC) a key technology for reducing content delivery latency. Unmanned Aerial Vehicles (UAVs) can serve as mobile aerial caching nodes that complement fixed ground infrastructure, but their small cache size and limited battery life restrict how long and how effectively they can operate. In addition, current approaches often optimize caching decisions, user association, and flight trajectories separately, without considering their interactions under tight energy constraints. In this paper, we formulate a joint optimization problem that aims to minimize the average content retrieval delay in an energy-constrained multi-UAV cooperative caching system. We then propose a deep reinforcement learning (DRL) framework based on the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithm, in which each UAV is trained as an independent agent under a centralized-training and decentralized-execution scheme. Simulation results show that our method outperforms several heuristic and non-cooperative reinforcement learning baselines in terms of cache hit rate and energy efficiency. Specifically, the proposed method reduces the system's average content retrieval delay with a maximum reduction of 9.1% and effectively guarantees an average cache hit rate of 62.45%, maintaining a sustained remaining energy margin over baseline methodologies.