Extended reality (XR), blending virtual and real worlds, is a key application of future networks. While AI advancements enhance XR capabilities, they also impose significant computational and energy challenges on lightweight XR devices. In this paper, we developed a distributed queue model for multi-task DNN inference, addressing issues of resource competition and queue coupling. In response to the challenges posed by the high energy consumption and limited resources of XR devices, we designed a dual time-scale joint optimization strategy for model partitioning and resource allocation, formulated as a bi-level optimization problem. This strategy aims to minimize the total energy consumption of XR devices while ensuring queue stability and adhering to computational and communication resource constraints. To tackle this problem, we devised a Lyapunov-guided Proximal Policy Optimization algorithm, named LyaPPO. Numerical results demonstrate that the LyaPPO algorithm outperforms the baselines, achieving energy conservation of 24.79% to 46.14% under varying resource capacities. Specifically, the proposed algorithm reduces the energy consumption of XR devices by 24.29% to 56.62% compared to baseline algorithms.

本研究针对XR设备在多任务深度神经网络推理中的能源消耗和资源竞争问题，提出了一种双时间尺度联合优化策略以实现模型分区与资源分配的高效管理。通过设计Lyapunov引导的近端策略优化算法LyaPPO，实验证明该算法能有效降低XR设备的能源消耗，提升设备的性能与资源使用效率。

基于Lyapunov引导的强化学习方法实现MEC辅助XR设备的多任务深度神经网络推理能源优化