2025/08/22 by Jia, Lianchen, Zhou, Chao, Li, Chaoyang +2 · 1 citation
#FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Multimedia (cs.MM) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2508.16454
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 LingXi, the first large-scale deployed system for personalized adaptive video streaming based on user-level experience. 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 LingXi's superior performance. 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.