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.

翻译：低地球轨道（LEO）卫星通信是为偏远地区用户提供互联网连接的一种有前景的方法。鉴于视频很可能占据LEO卫星网络中的大部分流量（与互联网其他部分类似），本文提出了一种新颖的、面向视频感知的移动管理框架，专为LEO卫星网络设计。利用仿真模型与现实世界数据集，我们展示了切换策略和吞吐量预测算法在单用户和多用户视频流场景中的重要性。基于这些观察，我们提出了一组新颖的算法，能够联合选择卫星和视频比特率，以优化体验质量（QoE）。我们首先开发了基于模型预测控制（MPC）和强化学习（RL）的单用户算法，然后将其扩展以适应可能共享同一颗卫星的多个竞争用户。我们在RL设计中引入了集中训练与分布式推理，从而实现了具备全局视角的分布式策略。通过基于轨迹驱动的仿真和测试平台实验，我们证明了所提模型的有效性。我们向研究社区共享了代码和数据。