Optimizing Quality for Collaborative Video Viewing

Dan Phung; Giuseppe Valetto; Gail E. Kaiser; Suhit Gupta

Optimizing Quality for Collaborative Video Viewing
Phung, Dan
Valetto, Giuseppe
Kaiser, Gail E.
Gupta, Suhit
Computer Science
Persistent URL:
Columbia University Computer Science Technical Reports
Part Number:
Department of Computer Science, Columbia University
Publisher Location:
New York
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 the students. We present an architecture and adaptation model called AI2TV (Adaptive Internet Interactive Team Video), a system that allows geographically dispersed participants, possibly some or all disadvantaged in network resources, to collaboratively view a video in synchrony. AI2TV upholds the invariant that each participant will view semantically equivalent content at all times. Video player actions, like play, pause and stop, can be initiated by any of the participants and the results of those actions are seen by all the members. These features allow group members to review a lecture video in tandem to facilitate the learning process. We employ an autonomic (feedback loop) controller that monitors clients' video status and adjusts the quality of the video according to the resources of each client. We show in experimental trials that our system can successfully synchronize video for distributed clients while, at the same time, optimizing the video quality given actual (fluctuating) bandwidth by adaptively adjusting the quality level for each participant.
Computer science
Item views
text | xml
Suggested Citation:
Dan Phung, Giuseppe Valetto, Gail E. Kaiser, Suhit Gupta, , Optimizing Quality for Collaborative Video Viewing, Columbia University Academic Commons, .

Columbia University Libraries | Policies | FAQ