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

Sourya Saha, Saptarshi Debroy

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.

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

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.
Paper Structure (28 sections, 17 equations, 13 figures, 4 tables)

This paper contains 28 sections, 17 equations, 13 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Model
  4. System Architecture and Execution Modes
  5. Performance Model: VR Latency
  6. Power Model
  7. VR Headset Battery Model
  8. Optimization Problem Formulation
  9. DRL-driven Adaptive Execution Control
  10. DRL-driven Problem Translation
  11. State Representation
  12. Action Space and Control Knobs
  13. Reward Design and Latency-Prioritized Optimization
  14. DRL Policy Optimization
  15. Exploration Strategy
  16. ...and 13 more sections

Figures (13)

  • Figure 1: The non-trivial conflict between Local vs Remote Offloading of XR workloads.
  • Figure 2: Overview of the edge-assisted interactive XR system model
  • Figure 3: Reinforcement learning control architecture where the DQN agent continuously observes battery, network, and latency conditions to select optimal quality, IMU rate, and processing mode configurations that extend device lifetime under real-time constraints.
  • Figure 4: Energy characteristics of LOCAL, OFFLOAD, and RL-Adaptive execution strategies under stable network condition.
  • Figure 5: Performance characteristics of different execution strategies under stable network conditions, showing the trade-offs between energy efficiency and MTP latency behavior.
  • ...and 8 more figures