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

arXiv: https://arxiv.org/abs/2603.16823 · HTML: https://mlsanity.com/paper/2603.16823/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 10 of 10)

- [Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems](https://mlsanity.com/paper/2005.02459/) (2020)
- [Reinforcement Learning-driven Data-intensive Workflow Scheduling for Volunteer Edge-Cloud](https://mlsanity.com/paper/2407.01428/) (2024)
- [Infer-EDGE: Dynamic DNN Inference Optimization in 'Just-in-time' Edge-AI Implementations](https://mlsanity.com/paper/2501.18842/) (2025)
- [Minimizing the Motion-to-Photon-delay (MPD) in Virtual Reality Systems](https://mlsanity.com/paper/2301.10408/) (2023)
- [Detection and Recovery of Adversarial Slow-Pose Drift in Offloaded Visual-Inertial Odometry](https://mlsanity.com/paper/2509.07130/) (2025)
- [Benchmarking SLAM Algorithms in the Cloud: The SLAM Hive Benchmarking Suite](https://mlsanity.com/paper/2406.17586/) (2024)
- [Correlation-Based Device Energy-Efficient Dynamic Multi-Task Offloading for Mobile Edge Computing](https://mlsanity.com/paper/2110.00424/) (2021)
- [Experimental Evaluation of Interactive Edge/Cloud Virtual Reality Gaming over Wi-Fi using Unity Render Streaming](https://mlsanity.com/paper/2402.00540/) (2024)
- [Edge Rendering Architecture for multiuser XR Experiences and E2E Performance Assessment](https://mlsanity.com/paper/2406.07087/) (2024)
- [Towards Next Generation Immersive Applications in 5G Environments](https://mlsanity.com/paper/2507.20050/) (2025)

## Abstract (arXiv)

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.