Joint Optimization of Offloading, Scheduling, and Inferencing for MEC-Empowered AIGC Services

Generative Diffusion Model (GDM)-based AI-Generated Content (AIGC) services are rapidly emerging as powerful solutions for creating high-quality, personalized content across various domains, playing an essential role in shaping the future network landscapes. However, the traditional cloud-based implementation of AIGC services faces substantial challenges due to the heterogeneity of GDMs as well as their computation-intensive nature and the low-latency requirement. In this work, we investigate a Mobile Edge Computing (MEC)-empowered heterogeneous AIGC service scenario, where User Equipments (UEs) and Base Stations (BSs) deploy lightweight and heavyweight GDMs, respectively, to deliver different levels of AIGC services at the network edge. Specifically, when initiating an AIGC task, each UE can choose to process the task locally using lightweight GDMs, or offload and process the task on a BS using heavyweight GDMs. In such a scenario, we focus on the joint optimization of offloading, scheduling, and inferencing, aiming to minimize task delay and energy consumption, while maximizing content quality. To address the problem in an online and decentralized manner, we develop a Multi-Agent Proximal Policy Optimization (MAPPO)-based deep reinforcement learning algorithm in a centralized training and decentralized execution framework. Simulation results show that our proposed algorithm outperforms existing intelligent benchmarks, with the performance gains ranging from 5.1% to 13.9%.