Adaptive Internet Interactive Team Video

Dan Phung, Giuseppe Valetto, Gail Kaiser

Department of Computer Science, Columbia University Technical Report , CUCS-009-05, February 2005


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 AI2 TV (Adaptive Internet Interactive Team Video), a system that allows bor- derless, virtual students, possibly some or all disadvantaged in network resources, to collaboratively view a video in synchrony. AI2 TV upholds the invariant that each student will view semantically equivalent content at all times. Video player actions, like play, pause and stop, can be ini- tiated 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.



Columbia University Department of Computer Science