Immersive volumetric video streaming in extended reality (XR) demands ultra-low motion-to-photon (MTP) latency, which conventional edge-centric architectures struggle to meet due to per-frame computationally intensive rendering tightly coupled with user motion. To address this challenge, we propose an Open Radio Access Network (O-RAN)-integrated playback framework that jointly orchestrates radio, compute, and content resources in near real time (Near-RT) control loop. The system formulates the rendered-pixel ratio as a continuous control variable and jointly optimizes it over the Open Cloud (O-Cloud) compute, gNB transmit power, and bandwidth under a Weber-Fechner quality of experience (QoE) model, explicitly balancing resolution, computation, and latency. A Soft Actor-Critic (SAC) agent with structured action decomposition and QoE-aware reward shaping resolves the resulting high-dimensional control problem. Experiments on a 5G O-RAN testbed and system simulations show that SAC reduces median MTP latency by above $11\%$ and improves both mean QoE and fairness, demonstrating the feasibility of RIC-driven joint radio-compute-content control for scalable, latency-aware immersive streaming.

翻译：扩展现实（XR）中的沉浸式体视频流传输要求极低的运动到光子（MTP）延迟，而传统的以边缘为中心的架构由于每帧计算密集型的渲染与用户运动紧密耦合，难以满足这一要求。为应对这一挑战，我们提出了一种集成开放无线接入网（O-RAN）的回放框架，该框架在近实时（Near-RT）控制环路中协同编排无线、计算和内容资源。该系统将渲染像素比公式化为一个连续控制变量，并在韦伯-费希纳体验质量（QoE）模型下，联合优化开放云（O-Cloud）计算、gNB发射功率和带宽，明确平衡了分辨率、计算和延迟。一个采用结构化动作分解和QoE感知奖励塑形的软演员-评论家（SAC）智能体解决了由此产生的高维控制问题。在5G O-RAN测试平台上的实验和系统仿真表明，SAC将中位数MTP延迟降低了$11\%$以上，并同时提高了平均QoE和公平性，这证明了RIC驱动的无线-计算-内容联合控制对于可扩展、延迟感知的沉浸式流传输的可行性。