Over the past decade, adaptive video streaming technology has witnessed significant advancements, particularly driven by the rapid evolution of deep learning techniques. However, the black-box nature of deep learning algorithms presents challenges for developers in understanding decision-making processes and optimizing for specific application scenarios. Although existing research has enhanced algorithm interpretability through decision tree conversion, interpretability does not directly equate to developers' subjective comprehensibility. To address this challenge, we introduce ComTree, the first bitrate adaptation algorithm generation framework that considers comprehensibility. The framework initially generates the complete set of decision trees that meet performance requirements, then leverages large language models to evaluate these trees for developer comprehensibility, ultimately selecting solutions that best facilitate human understanding and enhancement. Experimental results demonstrate that ComTree significantly improves comprehensibility while maintaining competitive performance, showing potential for further advancement. The source code and appendix are available at https://github.com/thu-media/ComTree.

Control algorithms in production environments typically require domain experts to tune their parameters and logic for specific scenarios. However, existing research predominantly focuses on algorithmic performance under ideal or default configurations, overlooking the critical aspect of Tuning Potential. To bridge this gap, we introduce \texttt{Crucible}, an agent that employs an LLM-driven, multi-level expert simulation to turn algorithms and defines a formalized metric to quantitatively evaluate their Tuning Potential. We demonstrate \texttt{Crucible}'s effectiveness across a wide spectrum of case studies, from classic control tasks to complex computer systems, and validate its findings in a real-world deployment. Our experimental results reveal that \texttt{Crucible} systematically quantifies the tunable space across different algorithms. Furthermore, \texttt{Crucible} provides a new dimension for algorithm analysis and design, which ultimately leads to performance improvements. Our code is available at https://github.com/thu-media/Crucible.

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.

Adaptive video streaming has been studied for over 10 years and has demonstrated remarkable performance. However, adaptive video streaming is not an independent algorithm but relies on other components of the video system. Consequently, as other components undergo optimization, the gap between the traditional simulator and the real-world system continues to grow which makes the adaptive video streaming algorithm must adapt to these variations.In order to address the challenges facing industrial adaptive video streaming, we introduce a novel offline reinforcement learning framework called Backwave. This framework leverages history logs to reduce the sim-real gap. We propose new metrics based on counterfactual reasoning to evaluate its performance and we integrate expert knowledge to generate valuable data to mitigate the issue of data override. Furthermore, we employ curriculum learning to minimize additional errors.We deployed Backwave on a mainstream commercial short video platform, Kuaishou. In a series of A/B tests conducted nearly one month with over 400M daily watch times, Backwave consistently outperforms prior algorithms. Specifically, Backwave reduces stall time by 0.45% to 8.52% while maintaining comparable video quality and Backwave demonstrates improvements in average play duration by 0.12% to 0.16%, and overall play duration by 0.12% to 0.26%.

Congestion control has been a fundamental research focus in web transmission for over 30 years. However, with diverse network scenarios like cellular networks and WiFi, traditional models might no longer accurately describe current network conditions -- we empirically observe that the minimum round-trip time (RTTmin) still varies under different network conditions, challenging the assumption of its constancy in traditional models. In this paper, we model it as a normal distribution based on our measurements and propose a novel congestion control algorithm LingBo. LingBo consists of two phases: an offline trained decision model to achieve goals under different RTTmin distributions, and an online perception scheme to detect the current RTTmin distribution. We evaluate LingBo in various network environments and find it consistently performs well in terms of power metric and throughput compared to recent state-of-the-art baselines. Our code is available at https://github.com/thumedia/LingBo.

The low-light environment is an integral surrounding in real-world video analytic applications. Conventional wisdom claims that in order to adapt to the extensive computation requirement of the analytics model and achieve high inference accuracy, the overall pipeline should leverage a client-to-cloud framework that designs a cloud-based inference with on-demand video streaming. However, we show that due to the amplified noise, directly streaming the video in low-light scenarios can introduce significant bandwidth inefficiency.In this paper, we propose Owl, an intelligent framework to optimize the bandwidth utilization and inference accuracy for the low-light video analytic pipeline. The core idea of Owl is two-fold: on the one hand, we will deploy a light-weighted pre-processing module before transmission, through which we will get the denoised video and significantly reduce the transmitted data; on the other hand, we recover the information from the denoised video via an enhancement module in the server-side. Specifically, through well-designed training mechanism and content representation technique, Owl can dynamically select the best configuration for time-varying videos. Experiments with a variety of datasets and tasks show that Owl achieves significant bandwidth benefits, while consistently optimizing the inference accuracy.

The delay to obtain analysis results is an important metric in video analytics. Previous work has focused on reducing frame transmission and inference delay to optimize total delay. However, network fluctuations can cause frames to arrive at the backend simultaneously, leading to backend queuing delays. To address this issue, we propose Concerto, a joint front-and backend video analytics pipeline that optimizes both network transmission and backend queuing delays. The backend controls the frame queue, accelerating or skipping inference as needed to mitigate backend queuing delay. The frontend considers both delays when configuring frames to send, resulting in better total delay. Experiments show that Concerto significantly reduces backend queuing delay with minimal loss of accuracy.