Channel Set Adaptation: Scalable and Adaptive Streaming for Non-Linear Media

csa-300x206Technologies that stream linear media objects, such as audio and video, are in wide use on the Internet. Large groups of users regularly tune in to a variety of online programming, including radio shows, sports events, and news coverage. However, non-linear media objects, such as large 3D computer graphics models and visualization databases, have proven more difficult to stream at scale due to their interactive nature. The Channel Set Adaptation (CSA) project provides a framework that allows for the efficient streaming of non-linear datasets to large user groups. CSA allows individual clients to request custom data flows for interactive applications using standard broadcast or multicast join and leave operations. CSA scales to support very large user groups while continuing to provide interactive data access to independently operating clients.

In addition to the core CSA algorithm, this project examines many related topics such as alternative multicast algorithms (StrandCast), a general model for user-driven adaptation (GAL), and applications to digital museums.  More details on these various technologies can be found in the publications listed below.

Publications

Ketan Mayer-Patel and David Gotz. Scalable and Adaptive Streaming for Non-Linear Media. IEEE MultiMedia (Volume 14, Number 3, 2007).
[This article is available through the IEEE Digital Library. View the article by clicking here.]

David Gotz. Scalable and Adaptive Streaming for Non-Linear Media. ACM Multimedia, Santa Barbara, CA (2006).
[PDF, 329k]

David Gotz and Ketan Mayer-Patel. A Framework for Scalable Delivery of Digitized Spaces. International Journal on Digital Libraries (Volume 5, Number 3, 2005).
[This article is available from Springer Online First. View the article by clicking here.]

David Gotz. Channel Set Adaptation: Scalable and Adaptive Streaming for Non-Linear Media. UNC-CS Ph.D. Dissertation (2005).
[PDF, 1,707k]

David Gotz and Ketan Mayer-Patel. GAL: A Middleware Library for Multidimensional Adaptation. UNC-CS Technical Report TR05-023 (2005).
[PDF, 345k]

David Gotz and Ketan Mayer-Patel. Scalable and Adaptive Streaming for Non-Linear Media. UNC-CS Technical Report TR05-022 (2005).
[PDF, 175k]

Brian Begnoche, David Gotz, and Ketan Mayer-Patel. The Design and Implementation of StrandCast. UNC-CS Technical Report TR05-004 (2005).
[PDF, 77k]

David Gotz and Ketan Mayer-Patel. A General Framework for Multidimensional Adaptation. ACM Multimedia, New York City, New York (2004).
[PDF, 384k]

David Gotz. Supporting Adaptive Remote Access to Multiresolutional or Hierarchical Data for Large User Groups. ACM Multimedia Doctoral Symposium, New York City, New York (2004).
[PDF, 15k]

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