Colloquium Details
Scalable and Adaptive Streaming for Non-Linear Media
| Author: | David Gotz University of North Carolina |
|---|---|
| Date: | February 24, 2005 |
| Time: | 3:30 |
| Location: | 220 Deschutes |
| Host: | Reza Rejaie |
Abstract
The streaming of linear media objects, such as audio and video, has become ubiquitous on today's internet. Large groups of users regularly tune in to a wide 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 due to their interactive nature. In this talk, I present Channel Set Adaptation (CSA), 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 multicast join and leave operations. CSA scales to support very large user groups and still provides interactive data access to independently operating clients. I will discuss a motivating sample application for digital museums and present results from an experimental evaluation of CSA's performance.
Biography:
David Gotz is a Ph.D. Candidate in the Department of Computer Science at the University of North Carolina at Chapel Hill. He received a BS in computer science from the Georgia Institute of Technology in 1999 where he graduated with Highest Honors and received a Certificate in Economics. He received a Master of Science in Computer Science from the University of North Carolina at Chapel Hill in 2001. His research interests include multimedia systems, networking, and graphics.