Immersive extended reality (XR) applications introduce latency-critical workloads that must satisfy stringent real-time responsiveness while operating on energy- and battery-constrained devices, making execution placement between end devices and nearby edge servers a fundamental systems challenge. Existing approaches to adaptive execution and computation offloading typically optimize average performance metrics and do not fully capture the sustained interaction between real-time latency requirements and device battery lifetime in closed-loop XR workloads. In this paper, we present a battery-aware execution management framework for edge-assisted XR systems that jointly considers execution placement, workload quality, latency requirements, and battery dynamics. We design an online decision mechanism based on a lightweight deep reinforcement learning policy that continuously adapts execution decisions under dynamic network conditions while maintaining high motion-to-photon latency compliance. Experimental results show that the proposed approach extends the projected device battery lifetime by up to 163% compared to latency-optimal local execution while maintaining over 90% motion-to-photon latency compliance under stable network conditions. Such compliance does not fall below 80% even under significantly limited network bandwidth availability, thereby demonstrating the effectiveness of explicitly managing latency-energy trade-offs in immersive XR systems.

翻译：沉浸式扩展现实（XR）应用引入了对延迟极为敏感的工作负载，这些负载必须在满足严格实时响应性的同时运行于能量和电池受限的设备上，这使得在终端设备与邻近边缘服务器之间进行执行放置成为一个根本性的系统挑战。现有的自适应执行与计算卸载方法通常优化平均性能指标，未能充分捕捉闭环XR工作负载中实时延迟要求与设备电池寿命之间的持续交互。本文中，我们提出了一种面向边缘辅助XR系统的电池感知执行管理框架，该框架联合考虑了执行放置、工作负载质量、延迟要求以及电池动态特性。我们设计了一种基于轻量级深度强化学习策略的在线决策机制，该机制能够在动态网络条件下持续调整执行决策，同时保持较高的运动到光子延迟合规性。实验结果表明，与延迟最优的本地执行相比，所提方法在稳定网络条件下将预计设备电池寿命延长了高达163%，同时保持了超过90%的运动到光子延迟合规性。即使在网络带宽可用性显著受限的情况下，该合规性也不会低于80%，从而证明了在沉浸式XR系统中显式管理延迟-能量权衡的有效性。