Fresh Analysis of Streaming Media Stored on the Web

With the steady increase in the bandwidth available to end users and Web sites hosting user generated content, there appears to be more multimedia content on the Web than ever before. Studies to quantify media stored on the Web done in 1997 and 2003 are now dated since the nature, size and number of streaming media objects on the Web have changed considerably. Although there have been more recent studies characterizing specific streaming media sites like YouTube, there are only a few studies that focus on characterizing the media stored on the Web as a whole. We build customized tools to crawl the Web, identify streaming media content and extract the characteristics of the streaming media found. We choose 16 different starting points and crawled 1.25 million Web pages from each starting point. Using the custom built tools, the media objects are identified and analyzed to determine attributes including media type, media length, codecs used for encoding, encoded bitrate, resolution, and aspect ratio. A little over half the media clips we encountered are video. MP3 and AAC are the most prevalent audio codecs whereas H.264 and FLV are the most common video codecs. The median size and encoded bitrates of stored media have increased since the last study. Information on the characteristics of stored multimedia and their trends over time can help system designers. The results can also be useful for empirical Internet measurements studies that attempt to mimic the behavior of streaming media traffic over the Internet

Dynamic optimization of the quality of experience during mobile video watching

Mobile video consumption through streaming is becoming increasingly popular. The video parameters for an optimal quality are often automatically determined based on device and network conditions. Current mobile video services typically decide on these parameters before starting the video streaming and stick to these parameters during video playback. However in a mobile environment, conditions may change significantly during video playback. Therefore, this paper proposes a dynamic optimization of the quality taking into account real-time data regarding network, device, and user movement during video playback. The optimization method is able to change the video quality level during playback if changing conditions require this. Through a user test, the dynamic optimization is compared with a traditional, static, quality optimization method. The results showed that our optimization can improve the perceived playback and video quality, especially under varying network conditions