SABR通过预训练+微调提升视频码率自适应的泛化能力
SABR: A Stable Adaptive Bitrate Framework Using Behavior Cloning Pretraining and Reinforcement Learning Fine-Tuning
- 先用行为克隆预训练，再用强化学习微调，提升稳定性
- 在3G和4G+基准上平均排名最优，优于Pensieve等模型
- 适合需要应对复杂多变网络环境的实时视频系统
随着5G的到来，互联网进入以视频为核心的全新阶段。从TikTok到Bilibili等平台，在线视频服务正重塑用户观看习惯。自适应码率（ABR）控制被广泛认为是影响用户体验质量（QoE）的关键因素。近年来基于学习的ABR方法受到越来越多关注，但大多数方法在训练时仅依赖有限的网络追踪数据集，忽视真实网络条件的广泛分布特性，导致在分布外（OOD）场景下泛化能力差。为此，我们提出SABR，一种结合行为克隆（BC）预训练与强化学习（RL）微调的训练框架。同时引入ABRBench-3G和ABRBench-4G+两个基准，提供广覆盖训练轨迹及专用的分布外测试集，用于评估对未见网络条件的鲁棒性。实验结果表明，SABR在所提基准上相较Pensieve、Comyco和NetLLM实现了最佳平均排名。结果表明，SABR可在广泛分布条件下实现更稳定的训练，并提升对未见网络环境的泛化能力。
原文摘要 · Abstract (English)
With the advent of 5G, the internet has entered a new video-centric era. From short-video platforms like TikTok to long-video platforms like Bilibili, online video services are reshaping user consumption habits. Adaptive Bitrate (ABR) control is widely recognized as a critical factor influencing Quality of Experience (QoE). Recent learning-based ABR methods have attracted increasing attention. However, most of them rely on limited network trace sets during training and overlook the wide-distribution characteristics of real-world network conditions, resulting in poor generalization in out-of-distribution (OOD) scenarios. To address this limitation, we propose SABR, a training framework that combines behavior cloning (BC) pretraining with reinforcement learning (RL) fine-tuning. We also introduce benchmarks, ABRBench-3G and ABRBench-4G+, which provide wide-coverage training traces and dedicated OOD test sets for assessing robustness to unseen network conditions. Experimental results demonstrate that SABR achieves the best average rank compared with Pensieve, Comyco, and NetLLM across the proposed benchmarks. These results indicate that SABR enables more stable learning across wide distributions and improves generalization to unseen network conditions.
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