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A Novel Spatial-Temporal Learning Method for Enhancing Generalization in Adaptive Video Streaming
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AI
DOI：10.1109/TMC.2025.3588135.png)
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Abstract
En 中文
Adaptive video streaming has become a fundamental technology for video delivery. With the rise of deep reinforcement learning (DRL), streaming vendors are increasingly adopting DRL-driven adaptive bitrate (ABR) algorithms. In real-world deployments, most ABR approaches are developed with the aim of maintaining good performance across a wide variety of network environments. However, contrary to this expectation, our empirical findings show that even when trained on extensive real-world network trace data, these DRL-based ABR algorithms achieve only 43.1% to 48.9% of Quality-of-Experience (QoE) under highly diverse network conditions, which falls significantly short of the 100% optimum. We termed this problem as “ABR Under-Generalization”. To overcome this problem, we introduce BETA – a novel DRL-based ABR framework that incorporates both spatial and temporal learning mechanisms: 1) Spatially, BETA features a detector that flags the network conditions likely to cause poor performance, then trains specialized ABR models tailored for those conditions and 2) Temporally, BETA enhances its learning by incorporating multi-step decision experiences at each training epoch, enabling the trained model to account for long-term environmental dynamics. Comprehensive evaluations show that BETA outperforms state-of-the-art ABR algorithms, yielding average QoE gains of 19.4% to 50.9%, and achieving improvements of up to 244.1% under severely fluctuating network conditions.
Keywords:
Video streaming
mobile network
deep reinforcement learning
quality-of-experience
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