基于用户观看体验优化视频流，提升观看时长与流畅度。
Towards User-level QoE: Large-scale Practice in Personalized Optimization of Adaptive Video Streaming
- 用用户退出率建模体验，动态调整播放参数。
- 8%流量测试中，总观看时长增0.15%，卡顿降1.3%。
- 特别改善低网速用户，卡顿减少15%，适合大平台应用。
传统基于系统级服务质量（QoS）的优化方法在大规模流媒体系统中已接近性能瓶颈。如何将用户级质量体验（QoE）与算法目标对齐仍是未解难题。为此，我们提出 exttt{LingXi}，首个基于用户级体验的大规模个性化自适应视频流系统。该系统通过分析用户参与度，利用退出率作为核心指标，研究生产环境日志中 QoS 指标与退出率的关系，构建个性化退出率预测模型。结合蒙特卡洛采样与在线贝叶斯优化，迭代确定最优参数。在快手（国内最大短视频平台之一）上进行的大规模 A/B 测试（覆盖8%流量）表明， exttt{LingXi} 在所有用户中实现总观看时长提升0.15%、码率提高0.1%、卡顿时间降低1.3%；尤其对低带宽用户，卡顿时间减少15%。
原文摘要 · Abstract (English)
Traditional optimization methods based on system-wide Quality of Service (QoS) metrics have approached their performance limitations in modern large-scale streaming systems. However, aligning user-level Quality of Experience~(QoE) with algorithmic optimization objectives remains an unresolved challenge. Therefore, we propose \texttt{LingXi}, the first large-scale deployed system for personalized adaptive video streaming based on user-level experience. \texttt{LingXi} dynamically optimizes the objectives of adaptive video streaming algorithms by analyzing user engagement. Utilizing exit rate as a key metric, we investigate the correlation between QoS indicators and exit rates based on production environment logs, subsequently developing a personalized exit rate predictor. Through Monte Carlo sampling and online Bayesian optimization, we iteratively determine optimal parameters. Large-scale A/B testing utilizing 8\% of traffic on Kuaishou, one of the largest short video platforms, demonstrates \texttt{LingXi}'s superior performance. \texttt{LingXi} achieves a 0.15\% increase in total viewing time, a 0.1\% improvement in bitrate, and a 1.3\% reduction in stall time across all users, with particularly significant improvements for low-bandwidth users who experience a 15\% reduction in stall time.
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