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Adaptive Interactive Internet Team Video

Phung, Dan; Valetto, Giuseppe; Kaiser, Gail E.

The increasing popularity of distance learning and 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), a system that allows borderless, 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 of the students and the results of those actions are seen by all the other students. These features allow group members to review a lecture video in tandem to facilitate the learning process. We show in experimental trials that our system can successfully synchronize video for distributed students while, at the same time, optimizing the video quality given actual (fluctuating) bandwidth by adaptively adjusting the quality level for each student.

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Academic Units
Computer Science
Publisher
Department of Computer Science, Columbia University
Series
Columbia University Computer Science Technical Reports, CUCS-009-05
Published Here
April 26, 2011
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