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

2026-03-17

AI Summary

This work addresses the dual challenges of latency and energy consumption in running immersive extended reality (XR) applications on battery-constrained devices. The authors propose an edge-assisted, battery-aware execution management framework that, for the first time, jointly models battery lifetime and motion-to-photon latency compliance. Leveraging lightweight deep reinforcement learning, the framework dynamically determines task offloading decisions, execution locations, and workload quality levels to achieve synergistic latency-energy optimization under varying network conditions. Experimental results demonstrate that, compared to purely local execution, the proposed system improves device battery life by up to 163%, achieves over 90% latency compliance under stable network conditions, and maintains at least 80% compliance even under severely constrained networks.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• edge offloading
• latency constraints
• battery lifetime
• extended reality
• execution placement

Innovation

• Deep Reinforcement Learning
• Edge Offloading
• Battery-aware Optimization
• Latency Compliance
• XR Systems