Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelines

• URL: http://arxiv.org/abs/2603.16823v1
• Date: Tue, 17 Mar 2026 17:30:11 GMT
• Title: Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelines
• Abstract summary: Immersive extended reality (XR) applications introduce latency-critical workloads that must satisfy stringent real-time responsiveness.<n>Existing approaches to adaptive execution and computation offloading do not fully capture the sustained interaction between real-time latency requirements and device battery lifetime.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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