Environment Information Enhanced Neural Adaptive Bitrate Video Streaming for Intercity Railway

Abstract

Intercity railways are vital to modern transportation systems, providing high-speed and efficient connections between cities. With the increasing demand for onboard entertainment and real-time monitoring systems, ensuring high Quality of Experience (QoE) video transmission has become a critical challenge. The unique characteristics of intercity railways, such as predictable railway schedules, spatial routes, and passenger-induced tidal effects, offer significant opportunities for optimizing video transmission performance. However, existing video streaming solutions must fully leverage these characteristics, resulting in inefficient bandwidth utilization, unstable video quality, and frequent interruptions caused by rapid train velocity, frequent handovers, and fluctuating network loads. This paper proposes an Environmental Information Enhanced adaptive video streaming (EIE-ABR) scheme that integrates environmental information with advanced techniques to address these challenges. Firstly, the scheme employs Deep Reinforcement Learning (DRL) to model the dynamic relationship between train speed and base station distance, enabling proactive bitrate adjustments in response to fluctuating network conditions. Secondly, EIE-ABR uses seasonal trend decomposition (STL) to capture throughput variations driven by periodic patterns, such as railway schedules and tidal effects, as well as abrupt disruptions from handovers or link failures. By combining DRL with STL, EIE-ABR achieves accurate throughput prediction and adapts effectively to the highly dynamic intercity railway environment. Simulation results show that EIE-ABR outperforms existing ABR algorithms, achieving an 11.22% improvement in average QoE reward.