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.

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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.