动态调度渲染任务，提升XR实时体验质量
Dynamic XR Rendering Offloading Based on Feature-Based Quality Assessment
- 根据网络和延迟动态切换本地与边缘渲染
- 新感知指标在头动场景下仍保持高鲁棒性
- 适用于对延迟敏感的XR应用开发与优化
扩展现实（XR）应用对计算资源需求高且要求低延迟，尤其在实时渲染任务中。本文提出一个基于边缘计算的XR渲染测试平台，可根据网络状况与延迟约束，动态将渲染负载在XR客户端与边缘服务器间分配。该平台集成微软HoloLens 2头显、支持GPU的边缘服务器及基于HOLO Stream SDK的定制远程渲染工具包，实现本地与边缘渲染模式的实时无缝切换。为克服像素级质量度量在头部运动和帧异步到达下的局限性，提出一种基于深度特征嵌入与余弦相似性的感知评估指标，对空间和时间错位具有强鲁棒性。此外，设计基于上下文老虎机学习的控制器，联合优化感知质量与延迟，实时调整渲染部署策略。实验结果验证了该平台的可行性与性能，证明其在提供高质量、高交互性XR体验方面的有效性。
原文摘要 · Abstract (English)
Extended Reality (XR) applications demand intensive computation and low latency, especially for real-time rendering tasks. In this letter, we present an edge-aided XR rendering testbed that dynamically offloads rendering workloads between the XR client and the edge server built upon network conditions and latency constraints. The testbed integrates a Microsoft HoloLens 2 headset, a GPU-enabled edge server, and a customized remote rendering toolkit based on the HOLO Stream SDK, enabling seamless switching between local and edge rendering modes in real time. To overcome the limitations of pixel-level quality metrics under head movements and asynchronous frame arrivals, we propose a perceptual evaluation metric based on deep feature embeddings and cosine similarity, which remains robust to spatial and temporal misalignments. Furthermore, we design a contextual bandit learning controller to adapt rendering placement decisions in real time by jointly optimizing perceptual quality and latency. Experimental results demonstrate the feasibility and performance of our testbed, validating its effectiveness in delivering high-quality and interactive XR experiences.
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