Live streaming that supports real-time interaction has become increasingly popular. To support the ensuing requirements on low end-to-end latency, RTM, the state-of-the-art live streaming system at Douyin, replaces the HTTP-FLV streaming protocol with WebRTC. To tailor the WebRTC stack to the live streaming scenario, we focus on optimizing first-frame delay, startup video rebuffering, audio-to-video drift, and per-session CPU usage. Those are the top-priority metrics identified from an importance analysis with respect to two user engagement metrics, i.e., viewer penetration and viewing time. To date, WebRTC-based streaming in RTM has been in operation for 4 years, and serves billions of viewer sessions every day. It dramatically optimizes QoE metrics (e.g., end-to-end latency reduced by 54.5%), and delivers statistically significant user engagement gains (e.g., number of paid orders increased by 0.8%). In this paper, we report our deployment experiences comprehensively.

Light detection and ranging (lidar)-derived elevation data are commonly subjected to outliers due to the boundaries of occlusions, physical imperfections of sensors, and surface reflectance. Outliers have a serious negative effect on the accuracy of digital elevation models (DEMs). To decrease the impact of outliers on DEM construction, we propose a robust interpolation algorithm of multiquadric (MQ) based on a regularized least absolute deviation (LAD) technique. The objective function of the proposed method includes a regularization-based smoothing term and an LAD-based fitting term, respectively, used to smooth noisy samples and resist the influence of outliers. To solve the objective function of the proposed method, we develop a simple scheme based on the split-Bregman iteration algorithm. Results from simulated data sets indicate that when sample points are noisy or contaminated by outliers, the proposed method is more accurate than the classical MQ and two recently developed robust algorithms of MQ for surface modeling. Real-world examples of interpolating 1 private and 11 publicly available airborne lidar-derived data sets demonstrate that the proposed method averagely produces better results than two promising interpolation methods including regularized spline with tension (RST) and gridded data-based robust thin plate spline (RTPS). Specifically, the image of RTPS is too smooth to retain terrain details. Although RST can keep subtle terrain features, it is distorted by some misclassified object points (i.e., pseudooutliers). The proposed method obtains a good tradeoff between resisting the effect of outliers and preserving terrain features. Overall, the proposed method can be considered as an alternative for interpolating lidar-derived data sets potentially including outliers.