No abstract

Video streaming platforms like Twitch.tv or YouNow have attracted the attention of both users and researchers in the last few years. Users increasingly adopt these platforms to share user-generated videos while researchers study their usage patterns to learn how to provide better and new services.In this paper, we focus on the YouNow platform and show the results of an analysis of its traffic patterns and other character istics. To perform this analysis, we have collected YouNow usage patterns for 85994 users over a period of about one month.Our results show that YouNow's characteristics are in part equal to and in part different from those of other video streaming platforms. Like on You Tube or Twitch.tv, for instance, few YouNow videos attract most of the view requests. On the other side, YouNow sessions are notably shorter than Twitch.tv ones.We believe the observation of these similarities and differences to be crucial to inform the design and implementation of better upcoming video streaming services.

Video sharing sites such as Youtube are prominent examples for the increasing demand of private camera owners to record, save and share their real life experiences as videos. Whereas professional video productions use advanced camera equipment in a controlled environment with skilled cameramen, User-Generated Video (UGV) differs significantly in regard of equipment, skill and thus in video quality. In this work we focus on a detailed analysis of the effects of camera shakes, harmful occlusions as well as a possible misalignment between the recording camera and the scene, on the resulting video quality. In contrast, most of the video quality discussion in the literature has focused on encoding artifacts and other compression problems. Our data was systematically gathered using large crowdsourcing experiments over three genres of video, and it was validated in a controlled lab setting. Our results show that even minimal camera shaking as well as occlusions lead to a significant reduction in the perceived video quality.

Although the importance of video sharing and of social media is increasing from day to day, a full integration of videos into social media is not achieved yet. We have developed a system that maps the concept of hypervideo -allowing to annotate objects in a video -to social media. We define this combination as social video that simultaneously allows a large number of users to contribute to the content of a video. Users can annotate video objects by adding images, text, other videos, Web links, or even communication topics. An integrated chat system allows users to communicate with friends and to link these topics to distinct objects in the video. We analyze the technical functionality and the user acceptance of our social video system in detail. Due to the integration into the social network Facebook more than 12,000 users have already accessed our system.

Adaptive video streaming systems rely on the availability of different quality versions of a video. Such a system can dynamically adjust the quality of a video stream during its playback depending on the available network throughput. Even if the necessary throughput is available, mobile users can benefit from limiting the generated data traffic as most cellular network contracts have data caps. Usually, if the cap is reached, the throughput is throttled to a speed that does not allow video streaming. Existing systems react to varying network conditions but often neglect content-specific adaptation needs. Content inspection can help to save data traffic when a higher bitrate representation would not increase the perceived quality. In this work, we present the Video Adaptation Service (VAS), a support service for a content-aware video adaptation for mobile devices. Based on the video content, the adaptation process is improved for both the available network resources and the perception of the user. By leveraging the content properties of a video stream, the system is able to maintain a stable video quality and at the same time reduce the generated data traffic. The system is evaluated with different adaptation schemes and shows that content-specific adaptation can both increase the perceived quality as well as reduce the data traffic. Additionally, we demonstrate the practical feasibility of this approach by integrating the VAS into Dynamic Adaptive Streaming over the Hypertext Transfer Protocol.