Scalable and adaptive streaming for non-linear media

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. This paper presents 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 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. We discuss a motivating sample application for digital museums and present results from an experimental evaluation of CSA's performance.

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