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Adaptive Internet Interactive Team Video

  • Dan Phung
  • Giuseppe Valetto
  • Gail Kaiser
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3583)

Abstract

The increasing popularity of online courses has highlighted the lack of collaborative tools for student groups. In addition, the introduction of lecture videos into the online curriculum has drawn attention to the disparity in the network resources used by students. We present an e-Learning architecture and adaptation model called AI2TV (Adaptive Internet Interactive Team Video), which allows virtual students, possibly some or all disadvantaged in network resources, to collaboratively view a video in synchrony. AI2TV upholds the invariant that each student will view semantically equivalent content at all times. Video player actions, like play, pause and stop, can be initiated by any student and their results are seen by all the other students. These features allow group members to review a lecture video in tandem, facilitating the learning process. Experimental trials show that AI2TV can successfully synchronize video for distributed students while, at the same time, optimizing the video quality, given fluctuating bandwidth, by adaptively adjusting the quality level for each student.

Keywords

Quality Level Video Quality Distance Education Video Server Compression Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dan Phung
    • 1
  • Giuseppe Valetto
    • 2
  • Gail Kaiser
    • 1
  1. 1.Columbia UniversityNew YorkUSA
  2. 2.Telecom Italia LabTurinItaly

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