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

2026-01-28

AI Summary

This work addresses the challenge of ultra-low motion-to-photon (MTP) latency in immersive volumetric video for extended reality (XR), where conventional edge architectures struggle to meet the stringent real-time demands arising from tight coupling between high-compute rendering and user motion. The authors propose a near-real-time streaming framework based on O-RAN that jointly orchestrates wireless, computing, and content resources. By optimizing the rendered pixel ratio through continuous control variables under constraints of O-Cloud compute capacity, gNB transmit power, and bandwidth, the framework holistically balances resolution, computational load, and latency. A novel integration of the Weber-Fechner perceptual model guides QoE-aware optimization, complemented by structured action decomposition and a QoE-sensitive reward mechanism to tackle high-dimensional control complexity. Experiments on a 5G O-RAN testbed and simulations demonstrate over an 11% reduction in median MTP latency, along with significant improvements in average QoE and user experience fairness.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Immersive volumetric video
• motion-to-photon latency
• extended reality
• resource allocation
• O-RAN

Innovation

• O-RAN
• volumetric video
• near-real-time control
• QoE-aware resource allocation
• Soft Actor-Critic