Adaptive gNB Control for Rapid, Reliable, and Diverse Video Transmission Beyond 5G

The broadcast industry aims to leverage 5G for rapid and ubiquitous high-quality video transmission. However, traditional network slices with preconfigured QoS settings for low-latency videos is insufficient to ensure high-quality video transmission, particularly in unstable wireless environments, which often leads to degraded image quality due to Layer 2 (L2) retransmission designed to preserve low latency. In response to this issue, we propose a system that anticipates potential video quality degradation and dynamically adjusts L2's retransmission settings via a Network Exposure Function (NEF). Previous studies have faced challenges with deep neural network (DNN) approaches, which despite being adaptive, require substantial training data, making them impractical or even prohibitive for broadcasters operating in diverse wireless conditions. In contrast, we propose a practical system that dynamically controls base station retransmission based on real-time radio and video quality assessments, aligning with the broadcasting industry's operational requirements. To facilitate rapid broadcasting startup, our system employs a Hidden Markov Model (HMM) for accurate radio quality prediction, requiring minimal training data. We implement our system using local 5G and a Commercial-Off-the-Shelf (COTS) UE and evaluate it in WebRTC video transmission. Our approach reduces Freeze Duration in deteriorating wireless scenarios from 3.88 to 1.91 seconds, significantly enhancing image quality stability. The system adapts to the wireless environment within approximately 280 seconds, demonstrating that HMM outperforms LSTM when only limited labled data is available.