VR Applications Joint Offloading and Scheduling Optimization in Multi-access Edge Computing^*

Multi-access edge computing (MEC) emerges as an effective computational paradigm. It meets the low latency and low energy consumption requirements of users by enabling user terminals (UE) to offload their computationally intensive applications to nearby access points (AP). However, the integration of Virtual Reality (VR) applications within MEC environments remains underexplored, primarily due to their structural complexity and high computational requirements for offloading and scheduling. To address these challenges, we use Directed Acyclic Graph (DAG) to model VR applications and 6G-oriented Rate-Splitting Multiple Access (RSMA) to enhance offloading and scheduling processes of VR applications. The objective is to jointly optimize the offloading strategy, transmit power, RSMA decoding order, and UE application scheduling to minimize the total system cost. Recognizing the limitations of traditional learning-based methods, which struggle with convergence in multi-user scenarios, we decompose the optimization problem into two subproblems: application offloading and application scheduling. We then propose the PPOCO algorithm, which integrates reinforcement learning with convex optimization to effectively solve these subproblems independently. Experimental results demonstrate that our proposed method consistently out-performs baseline algorithms in reducing the total system cost across various MEC network configurations.