Learning Based Rate Adapter for UAV Streaming

The increasing demand for high-quality real-time 360° video streams from mobile platforms, such as 5G-connected Unmanned Aerial Vehicles (UAVs), is challenging modern B5G networks. Vehicular mobility and fluctuating conditions in high-altitude, high-speed scenarios, known as high volatility, complicate maintaining an effective Quality of Experience (QoE) for cellular networks. This work introduces FlyBit, a Deep Reinforcement Learning (DRL)-based bitrate selection framework for live 360° video streaming in 5G-connected UAV applications, designed to enhance video quality, reduce packet loss, and minimize End-to-End (E2E) latency. We developed and deployed a real-world testbed to evaluate the impact of dynamic network conditions, UAV mobility, and trajectory on streaming performance, analyzing FlyBit with real-world data. Experimental results show that FlyBit improves Video Multimethod Assessment Fusion (VMAF) by ~29% and average bitrate by ~50%, while maintaining low latency and packet loss compared to baseline approaches, demonstrating its ability to adjust bitrate in real-time and significantly improve QoE for ultra-low-latency video streaming.