# Towards User-level QoE: Large-scale Practice in Personalized Optimization of Adaptive Video Streaming

arXiv: https://arxiv.org/abs/2508.16454 · HTML: https://mlsanity.com/paper/2508.16454/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [Crucible: Quantifying the Potential of Control Algorithms through LLM Agents](https://mlsanity.com/paper/2510.18491/) (2025)

## Abstract (arXiv)

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 textttLingXi, the first large-scale deployed system for personalized adaptive video streaming based on user-level experience. textttLingXi 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 textttLingXi's superior performance. textttLingXi 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.