Immersive Volumetric Video Playback: Near-RT Resource Allocation and O-RAN-based Implementation

TL;DR

This paper presents a near-real-time resource allocation framework for immersive volumetric video streaming in XR, integrating radio, compute, and content control via O-RAN and reinforcement learning to reduce latency and enhance QoE.

Contribution

It introduces a novel O-RAN-based control system that jointly optimizes radio, compute, and content resources using a SAC agent for immersive video streaming.

Findings

Median MTP latency reduced by over 11%

Improved mean QoE and fairness in experiments

Feasibility demonstrated on 5G O-RAN testbed

Abstract

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.