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Aquarema in Action: Improving the YouTube QoE in Wireless Mesh Networks

Aquarema in Action: Improving the YouTube QoE in Wireless Mesh Networks. Speaker : Yu- Hui Chen Authors : Barbara Staehle , Matthias Hirth , Rastin Pries, Florian Wamser , Dirk Staehle From : 2011 Baltic Congress on Future Internet and Communications. outline. Introduction

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Aquarema in Action: Improving the YouTube QoE in Wireless Mesh Networks

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  1. Aquarema in Action: Improving theYouTube QoE in Wireless Mesh Networks Speaker : Yu-Hui Chen Authors : Barbara Staehle, Matthias Hirth, Rastin Pries, Florian Wamser, Dirk Staehle From : 2011 Baltic Congress on Future Internet and Communications

  2. outline • Introduction • Methodology • Experimental results • Conclusion

  3. 1.Introduction • The QoE of a YouTube user depends on factors as different as the video content, the video- and audio-quality, or the time until the playback starts. • The authors propose to dynamically constrain the bandwidth of best effort traffic in order to ensure the quality of service requirements of multimedia applications.(the traffic observer (TO) and thetraffic controller (TC).)

  4. 2.Methodology • A. The Technology Behind YouTube • B. The Main YoMoFunctionality • C. Estimating the Buffered Playtime

  5. A)The Technology Behind YouTube • The YouTube player is a proprietary Flash application which concurrently plays a Flash video (FLV) file and downloads it via HTTP. • FLV files may also contain metadata which contain information about the duration of the video, the audio and video rate, and the file size. • During the time of simultaneous playback and downloading. • Adual-threshold buffering strategy which means that at the beginning the download, the client fills an internal buffer and starts.

  6. B)The Main YoMo Functionality • YoMo runs at the client and parses all incoming flows. Consequently it recognizes each flow containing FLV data as soon as the header of an FLV file is found. • Once a flow containing FLV data is recognized, its data is continuously parsed in order to retrieve the available meta information from the FLV file. Detecting the YouTube flow is thus easily done.

  7. C)Estimating the Buffered Playtime • The cooperation with the Firefox plugin allows YoMo to estimate the video on average to stall roughly 0.1 sec earlier than it actually did . • In most cases, YoMo underestimates the remaining play time, i.e. predicts the time of stalling earlier than it actually happened.

  8. t: the current time of the video T: already downloaded playtime B<Ba: threshold

  9. 3. Experimental results

  10. OTC:bandwidthshaping tool

  11. 5.Conclusion • YoMo which is able to identify a YouTube video and to determine its buffered playtime. • YoMo is able to detect an imminent QoE degradation. • YoMo and the bandwidth shaper OTC successfully avoidsstall times for the case where the cross traffic is on a link usedby the YouTube flow as well as on an interfering link.

  12. Thank you

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