Core Viewpoint - Kuaishou and Tsinghua University's Sun Lifeng team have developed the LingXi system, a groundbreaking personalized optimization system for adaptive video streaming, which has been accepted at the prestigious ACM SIGCOMM 2025 conference [2][4]. Group 1: Background and Motivation - The transition from traditional Quality of Service (QoS) to personalized Quality of Experience (QoE) is highlighted, emphasizing the limitations of existing QoS optimization methods in enhancing user experience [6]. - A large-scale A/B test demonstrated that traditional QoS metrics do not translate into improved user experience, indicating a saturation of optimization paths [7][14]. - The study identifies "buffering" as the primary negative factor affecting user experience, necessitating a focus on this aspect for effective QoE optimization [15][23]. Group 2: System Design and Components - The LingXi system is designed as a dynamic optimization module compatible with existing Adaptive Bitrate (ABR) algorithms, ensuring seamless integration without disrupting user experience [31][34]. - The system comprises three core components: 1. Online Bayesian Optimization (OBO) for dynamic parameter exploration [34]. 2. Monte Carlo Sampling for simulating future decisions based on historical data [35]. 3. Hybrid Exit Rate Predictor for accurately quantifying user experience [36][38]. Group 3: Experimental Results - A 10-day large-scale A/B test on the Kuaishou platform showed significant improvements in both QoE and QoS metrics, validating the effectiveness of the LingXi system [40][46]. - The system particularly benefits low-bandwidth users, reducing buffering time by approximately 15% in scenarios with bandwidth below 2000 kbps [52][58]. - The analysis of user sensitivity to buffering revealed a clear negative correlation between buffering sensitivity and the parameters assigned by the system, demonstrating the system's ability to adapt to individual user needs [56]. Group 4: Conclusion - The successful implementation of the LingXi system marks a significant evolution in adaptive video streaming optimization, shifting from static system-level goals to personalized strategies for diverse user experiences [57][58].