Home

Adaptive Interactive Internet Team Video

Dan Phung; Giuseppe Valetto; Gail E. Kaiser

Title:
Adaptive Interactive Internet Team Video
Author(s):
Phung, Dan
Valetto, Giuseppe
Kaiser, Gail E.
Date:
Type:
Technical reports
Department:
Computer Science
Permanent URL:
Series:
Columbia University Computer Science Technical Reports
Part Number:
CUCS-009-05
Publisher:
Department of Computer Science, Columbia University
Publisher Location:
New York
Abstract:
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.
Subject(s):
Computer science
Item views:
266
Metadata:
text | xml

In Partnership with the Center for Digital Research and Scholarship at Columbia University Libraries/Information Services | Terms of Use