Audiences cangain an immersive experience watching videos from multiple angles (a.k.a. viewpoints). Free Viewpoint Video (FVV) is developed to enable users to choose their preferred viewpoints during the play of a video. However, users may experience a delay if video frames of the chosen viewpoint cannot be timely loaded, or synthesized from multiple video streams of neighboring viewpoints. To address this problem, we present Edge-FVV, an edge-assisted FVV system that employs edge caches to reduce the delay in streaming the requested FVV from the server to client users. We first analyze the capacity and delay at edge caches when answering FVV requests. Next, we propose two types of machine learning algorithms that allocate the users’ requests to appropriate edge caches. Our evaluation shows that two types of proposed algorithms outperform benchmarks by 4.2-7.4% and 4.6-6.8%, respectively, in reducing the delay for FVV requests.

Today’s video service providers all desire to deeply understand the ever changing factors on user QoE to attract more users. In this paper, we study the user engagement with respect to video quality metrics and improve user engagement in adaptive video streaming systems. We conduct a comprehensive study of the real data from iQIYI, covering 700K users and 150K videos. We find bitrate switch becomes the new dominant factor on user engagement instead of rebuffering events. We also observe the impact of rate of rebuffering is more dominant than rebuffering time. We examine novel interdependencies between quality metrics in the system, e.g., the positive correlation between bitrate switch and average bitrate, which is due to the context system strategy, i.e., conservative bitrate enhancing strategy adopted by iQIYI. To improve user engagement, we propose a new engagement centric QoE function based on real data and design server side ABR algorithm which leverages our new QoE function. We evaluate our method for online test in iQIYI, with 490K real users viewing 666K streams. The results show our approach outperforms existing approaches by significantly improving the viewing time, i.e., 2.8 minutes longer viewing time per user.

The 360-degree video streaming system delivers a monocular panoramic video surrounding the user, and the user can change the viewing direction of mobile devices to see different parts of the video through the “viewport”. Due to the limited network bandwidth, playbacks of high-resolution 360-degree videos often suffer from rebuffering, while too much bandwidth is wasted in delivering those out-of-viewport parts that the user never watches. In this article, we present an Ensemble Prediction and Allocation based Streaming System, named as EPASS360, for delivering high Quality of Experience (QoE) 360-degree videos. The prediction model takes advantages of ensemble learning, providing high accuracy on the prediction of viewports. The allocation model divides a video into tiles, and allocates high resolution to tiles where a user's viewpoint may appear in the future by solving the QoE-aware optimization problem. Trace-driven emulation on real-world datasets shows that EPASS360 enhances the QoE in various scenarios compared to state-of-the-art streaming approaches. Experiments on the head-mounted device and the hand-held device over real-world Internet confirm the high user experience of EPASS360.

Given high-speed mobile Internet access today, audiences are expecting much higher video quality than before. Video service providers have deployed dynamic video bitrate adaptation services to fulfill such user demands. However, legacy video bitrate adaptation techniques are highly dependent on the estimation of dynamic bandwidth, and fail to integrate the video quality enhancement techniques, or consider the heterogeneous computing capabilities of client devices, leading to low quality of experience (QoE) for users. In this paper, we present a super-resolution based adaptive video streaming (SRAVS) framework, which applies a Reinforcement Learning (RL) model for integrating the video super-resolution (VSR) technique with the video streaming strategy. The VSR technique allows clients to download low bitrate video segments, reconstruct and enhance them to high-quality video segments while making the system less dependent on estimating dynamic bandwidth. The RL model investigates both the playback statistics and the distinguishing features related to the client-side computing capabilities. Trace-driven emulations over real-world videos and bandwidth traces verify that SRAVS can significantly improve the QoE for users compared to the state-of-the-art video streaming strategies with or without involving VSR techniques.

In the past few years, virtual reality (VR) has become an enabling technique, not only for enriching our visual experience but also for providing new channels for businesses. Untethered mobile devices are the main players for watching 360-degree content, thereby the precision of predicting the future viewpoints is one key challenge to improve the quality of the playbacks. In this paper, we investigate the image features of the 360-degree videos and the contextual information of the viewpoint trajectories. Specifically, we design ladder convolution to adapt for the distorted image, and propose LadderNet to transfer the knowledge from the pre-trained model and retrieve the features from the distorted image. We then combine the image features and the contextual viewpoints as the inputs for long short-term memory (LSTM) to predict the future viewpoints. Our approach is compared with several state-of-the-art viewpoint prediction algorithms over two 360-degree video datasets. Results show that our approach can improve the Intersection over Union (IoU) by at least 5% and meeting the requirements of the playback of 360-degree video on mobile devices.

Adaptive Bitrate Algorithm (ABR) is a category of important technique to improve the Quality of Experience (QoE) of video streaming service. Many ABR algorithms have been proposed recently and achieved good results. However, there are still some problems to be solved: (1) These ABR algorithms are based on open source data and evaluated by only one simulated player; (2) The QoE metrics are defined differently and their parameters are set by experience with little explanation; (3) These algorithms consider only the QoE of users but ignore the bandwidth cost of video service providers. In order to solve these problems, we propose a Real-time Evaluation System for ABR (RESA) which do evaluations by real online users. A reasonable QoE metric is also introduced by setting its parameters based on user preferences during video watching. We evaluate the state-of-the-art ABR algorithms on RESA with real video streaming service. Finally, we further introduce bitrate controlling into the Adaptive Bitrate Algorithm to solve the bandwidth cost problem for online ABR.

Online video service has become an emerging application of Internet, playing an important and indispensable role in our daily life. Aiming at relieving the heavy burden on CDN servers, many video service providers deploy a hybrid CDN-P2P system. To minimize the cost of the "last mile delivery" of CDN servers, the system should be able to dispatch video files that receive a large amount of view requests during peak hours to P2P network in advance. However, to accurately predict whether a video will gain popularity is difficult due to the uncertainty of people's viewing tendency. In this paper, we reveal the correlation between the popularity of videos in the daytime and the popularity in peak hours at night based on statistics collected in a real-world large commercial VoD system. Based on our findings, we propose Adaptive Time-Dependent Push Strategy (ATDPS), a scalable push strategy that implements adaptive time-dependent techniques in the hybrid CDN-P2P system. A deep neural network is leveraged to predict the demand of hot videos during peak hours at night. Besides, the system adaptively adjusts the time-dependent strategy of pushing the content of hot videos to the P2P network according to the seed scarcity of the videos in different time periods. Simulations over historical data and pilot deployment on smart routers show that our ATDPS can significantly decrease the addressed bandwidth consumption at peak hours.

Decentralizing content delivery to edge devices has become a popular solution for saving the bandwidth consumption of CDN when the CDN bandwidth is expensive. One successful realization is the hybrid CDN-P2P VoD system, where a client is allowed to request video content from a number of seeds (seed clients) in the P2P network. However, the seed scarcity problem may arise for a video resource when there are an insufficient number of seeds to satisfy requests to the video. To alleviate this problem, many commercial VoD systems have employed a video push mechanism that directly sends the recent scarce video resources to randomly-chosen seeds to serve more requests. However, the current video push mechanism fails to consider which videos will become scarce in the future, or differentiate the uploading capability of different seeds. In this paper, we propose Proactive-Push, a video push mechanism that lowers the bandwidth consumption of CDN by predicting future scarce videos and proactively sending them to competent seeds with strong uploading capabilities. Proactive-Push trains neural network models to correctly predict 80% of future scarce video resources, and identify over 90% of competent seeds. We evaluate Proactive-Push using a trace-driven emulation and a real-world pilot deployment over a commercial VoD system. Results show that Proactive-Push can further reduce the proportion of direct download from CDN by 21%, and save the CDN bandwidth cost at peak time by 18%.