To reduce costs and improve performance, video Content Delivery Networks (CDNs) have started to incorporate lightweight edge nodes, e.g., WiFi access points. Because of this, it is necessary for CDNs to intelligently select which video files should be placed at their core data centers vs. these edge nodes. This is more complex than traditional CDN management, as lightweight edge nodes are much more numerous and unstable than data centers. With this in mind, we present SDPush —- a system for managing content placement in edge CDNs. SDPush tackles two problems. First, it is necessary for SDPush to select which files to proactive push. To address this, we build a file popularity prediction model that effectively identifies video files that will receive many views. Second, SDPush should determine how many replicas of each file to push. To address this, we design a model to predict the benefits of pushing particular files (regarding traffic savings) and then formulate the replica decision problem as a lightweight problem, which is solvable within seconds, even for platforms that accommodate millions of daily active users. Through a trace-driven evaluation and a live deployment on a real video platform, we validate SDPush’s effectiveness, offloading peak-period traffic by 12.1% to 23.9% from the data center to edge nodes, thereby reducing the CDN costs.

Recently, we have observed an exponential increase of user-generated content (UGC) videos. The distinguished characteristic of UGC videos originates from the video production and delivery chain, as they are usually acquired and processed by non-professional users before uploading to the hosting platforms for sharing. As such, these videos usually undergo multiple distortion stages that may affect visual quality before ultimately being viewed. Inspired by the increasing consensus that the optimization of the video coding and processing shall be fully driven by the perceptual quality, in this paper, we propose to study the quality of the UGC videos from both objective and subjective perspectives. We first construct a UGC video quality assessment (VQA) database, aiming to provide useful guidance for the UGC video coding and processing in the hosting platform. The database contains source UGC videos uploaded to the platform and their transcoded versions that are ultimately enjoyed by end-users, along with their subjective scores. Furthermore, we develop an objective quality assessment algorithm that automatically evaluates the quality of the transcoded videos based on the corrupted reference, which is in accordance with the application scenarios of UGC video sharing in the hosting platforms. The information from the corrupted reference is well leveraged and the quality is predicted based on the inferred quality maps with deep neural networks (DNN). Experimental results show that the proposed method yields superior performance. Both subjective and objective evaluations of the UGC videos also shed lights on the design of perceptual UGC video coding.

On a platform of user-generated content (UGC), the uploaded videos need to be encoded again before distribution. For this specific encoding scenario, we propose a novel dataset and a corresponding learning-based scheme that is able to achieve significant bitrate saving without decreasing perceptual quality. In the dataset, each video's label indicates whether it can be encoded with a much lower bitrate while still keeps the same perceptual quality. Models trained on this dataset can then be used to classify the input video and adjust its final encoding parameters accordingly. With enough classification accuracy, more than 20% average bitrate saving can be obtained through the proposed scheme. The dataset will be further expanded to facilitate the study on this problem.

Short video applications, like TikTok, have attracted many users across the world. It can feed short videos based on users' preferences and allow users to slide the boring content anywhere and anytime. To reduce the loading time and keep playback smoothness, most of the short video apps will preload the recommended short videos in advance. However, these apps preload short videos in fixed size and fixed order, which can lead to huge playback stall and huge bandwidth waste. To deal with these problems, we present an Adaptive Preloading mechanism for short videos based on Lyapunov Optimization, also called APL, to achieve near-optimal playback experience, i.e., maximizing playback smoothness and minimizing bandwidth waste considering users' sliding behaviors. Specifically, we make three technical contributions: (1) We design a novel short video streaming framework which can dynamically preload the recommended short videos before the current video is downloaded completely. (2) We formulate the preloading problem into a playback experience optimization problem to maximize the playback smoothness and minimize the bandwidth waste. (3) We transform the playback experience optimization problem during the whole viewing process into a single-step greedy algorithm based on the Lyapunov optimization theory to make the online decisions during playback. Through extensive experiments based on the real datasets that generously provided by TikTok, we demonstrate that APL can reduce the stall ratio by 81%/12% and bandwidth waste by 11%/31% compared with no-preloading/fixed-preloading mechanism.

TCP-based application-layer protocols are increasingly applied to commercial live video streaming systems. However, in unstable mobile networks, the throughput of TCP may fluctuate rapidly due to its transmission mechanism, causing undesired playback interruption. In this paper, we propose a general application-layer rate adaptation approach to cope with the variability in TCP throughput. We analyze the transmission process and evaluate the network condition using a multi-buffer model. With information obtained in this model, an algorithm based on the Proportional-Integral-Derivative (PID) controller is proposed to dynamically adjust the video bitrate in response to the throughput change. We have implemented an experimental mobile live streaming system employing this approach and achieved a significant improvement in playback continuity and bandwidth utilization.

To cope with the challenges brought about by bandwidth fluctuation and improve the experience of watching online videos, an adaptive video streaming system that can adjust video quality according to actual network conditions is proposed based on the scalable video coding (SVC) extension of H.264/AVC. First, a simple and effective linear error model is proposed and verified for quality scalability of SVC. The model exploits the linear feature of pixel value errors and can be used to accurately estimate the distortion caused by discarding any combination of enhancement data packets in an SVC bitstream. On that basis, a greedy-like algorithm is designed to assign each data packet a priority value according to its rate-distortion (R-D) impact, thus enabling R-D optimized bitstream extraction under certain bitrate constraints. Finally, the proportional-integral-derivative (PID) method is utilized to control the video quality adjustment and determine a suitable bitrate for transmission. By monitoring and predicting the past, current, and future bandwidth information, the PID-based quality control algorithm is able to reduce quality fluctuation, while still preserving a high quality level. Experimental results show that compared with the baseline software, the proposed system that integrates the above algorithms can achieve much lower video quality fluctuation, with PSNR variance reduced from 1.24 to 0.69, and at the same time deliver higher video quality, with the PSNR average increased by 0.83 dB.

This paper investigates the no-reference SVC bitstream extraction problem and presents an efficient solution to approximate the “optimal” extracted sub-stream. First, we introduce a linear error model to accurately estimate the distortion caused by discarding any combination of packets, even when the original sequence is not available. Then we propose a greedy algorithm to decide each packet's priority according to its R-D impact. The priority value of packets can be stored in the bitstream and used for R-D optimized extraction. Experimental results show that our bitstream extraction method can achieve a significant PSNR gain compared to the extractors of JSVM, without computational complexity increment. Comparison with other methods also demonstrates the advantage of the proposed method.

Scalable Video Coding (SVC) makes it possible to change video quality dynamically according to real-time bandwidth. For quality control algorithms of SVC video streaming, the biggest challenge is to keep a video quality that is both smooth and as good as possible. In this paper, we first introduce a combined quality level scheme to describe SVC video quality in a unified way. Then an effective and efficient quality control algorithm for SVC video streaming is proposed based on the Proportional-Integral-Derivative (PID) control method. Extensive experiments show that the proposed algorithm improves 8.6% in video quality with 24.8% reduction in quality fluctuation compared with the existing packet delay feedback algorithm. The proposed algorithm has also been implemented in online video website www.7dlive.com and performs well in applications.

In this paper, we propose a novel design and optimized implementation of the HEVC decoder. First, a novel decoder prototype with refined decoding workflow and efficient memory management is designed. Then on this basis, a series of single-instruction-multiple-data (SIMD) based algorithms are used to speed up several time-consuming modules in HEVC decoding. Finally, a frame-based parallel framework is applied to exploit the multi-threading technology on multicore processors. With the highly optimized HEVC decoder, decoding speed of 246fps on Intel i7-2400 3.4GHz quad-core processor for 1080p videos and 52fps on ARM Cortex-A9 1.2GHz dual-core processor for 720p videos can be achieved in our experiments.