Joint Optimization of Handoff and Video Rate in LEO Satellite Networks
For users of LEO satellite networks, this work addresses the critical problem of video streaming quality degradation due to frequent handoffs, providing a practical solution that outperforms existing methods.
This work introduces a video-aware mobility management framework for LEO satellite networks that jointly optimizes handoff and video bitrate selection to improve Quality of Experience (QoE). Using MPC and RL algorithms, the approach achieves significant QoE gains over baseline methods in trace-driven simulations and testbed experiments.
Low Earth Orbit (LEO) satellite communication is a promising approach to providing Internet connectivity to users in many remote areas. As videos are likely to account for most traffic in the LEO satellite network, as in the rest of the Internet, this work introduces a novel video-aware mobility management framework tailored for LEO satellite networks. Utilizing simulation models alongside real-world datasets, we show the importance of handoff strategy and throughput prediction algorithms in single-user and multi-user video streaming scenarios. Motivated by these observations, we propose a set of novel algorithms that can jointly choose the satellite and video bitrate to optimize the Quality of Experience (QoE). We first develop Model Predictive Control (MPC) and Reinforcement Learning (RL) based algorithms for a single user, and then extend them to accommodate multiple competing users that may share the same satellite. We introduce centralized training and distributed inference for our RL design, enabling a distributed policy informed by a global perspective. We demonstrate the effectiveness of our proposed models using trace-driven simulation and testbed experiments. We share our code and data with the research community.