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

TL;DR

The paper addresses buffering and latency spikes in latency-sensitive live video over variable wireless links by moving from reactive bitrate adaptation to proactive cross-layer control. It introduces a semantic control channel within an ORAN/MEC framework, where a MEC-hosted Generative AI (GAI) module reconstructs high-quality frames from downscaled content, enabling continued high perceptual quality under degraded uplink conditions. Key contributions include an ORAN-compliant intelligent architecture, experimental validation on a live testbed with 50 video streams showing up to $4$ dB PSNR and $15$ points in VMAF gains, and latency-tail reduction by preventing spikes above $600$ ms. The work demonstrates the practical viability of cross-layer coordination and edge-assisted, content-aware upscaling for latency-critical video, with implications for URLLC/EMBB scenarios and future MEC-enabled radio networks.

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.

Figures (7)

• Figure 1: Proposed system for GAI-based MEC and CN.
• Figure 2: Application of GAI in a video streaming context.
• Figure 3: Experimental lab setup.
• Figure 4: CDF of PSNR under different downscaling factors ($\epsilon$).
• Figure 5: CDF of VMAF for different downscaling factors ($\epsilon$).