Experimental Study of Low-Latency Video Streaming in an ORAN Setup with Generative AI

TL;DR

This paper presents a proactive, AI-driven approach for low-latency live video streaming in ORAN networks, reducing latency spikes and improving video quality through cross-layer coordination and generative AI reconstruction.

Contribution

It introduces a novel semantic control channel and AI-based frame reconstruction method for proactive latency management in ORAN live video streaming.

Findings

Reduces latency tail behavior and latency spikes.

Achieves up to 4 dB PSNR improvement.

Gains 15 points in VMAF.

Abstract

Current Adaptive Bit Rate (ABR) methods react to network congestion after it occurs, causing application layer buffering and latency spikes in live video streaming. We introduce a proactive semantic control channel that enables coordination between Open Radio Access Network (ORAN) xApp, Mobile Edge computing (MEC), and User Equipment (UE) components for seamless live video streaming between mobile devices. When the transmitting UE experiences poor Uplink (UL) conditions, the MEC proactively instructs downscaling based on low-level RAN metrics, including channel SNR updated every millisecond, preventing buffering before it occurs. A Generative AI (GAI) module at the MEC reconstructs high-quality frames from downscaled video before forwarding to the receiving UE via the typically more robust Downlink (DL). Experimental validation on a live ORAN testbed with 50 video streams shows that our approach reduces latency tail behavior while achieving up to 4 dB improvement in PSNR and 15 points in VMAF compared to reactive ABR methods. The proactive control eliminates latency spikes exceeding 600 ms, demonstrating effective cross-layer coordination for latency-critical live video streaming.