Channel Set Adaptation: 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. In this dissertation, I present a complete framework for the efficient streaming of non-linear datasets to large user groups.

The framework has several components. First, I present the Representation Graph, an abstract data representation for expressing the semantic and syntactic relationships between elements of information in a non-linear multimedia database.

I then present a computational model for achieving multidimensional adaptation. The model is based on a spatially defined utility metric that allows applications to mathematically express trade-offs between different dimensions of adaptivity.

The representation graph and adaptation model serve as the foundation for the Generic Adaptation Library (GAL). GAL defines a layered design for non-linear media applications and provides an implementation for the middle adaptation layer.

The representation graph, adaptation model, and GAL can be combined to support adaptive non-linear streaming. I evaluate the performance of an experimental prototype based on these principles and show that they can effectively support the adaptive requirements of dynamic and interactive access to non-linear media.

I also define Channel Set Adaptation (CSA), an architecture for scalable delivery of non-linear media. CSA maps concepts from the representation graph to a scalable subscription-based network model. CSA provides, in the ideal case, an infinitely scalable streaming solution for non-linear media applications. I include a evaluation of CSA’s performance on both multicast and broadcast networks.

In addition, I develop a performance model based on results from the experimental evaluation. The performance model highlights several key properties of the underlying communication model that are most important to CSA performance.

University of North Carolina at Chapel Hill Department of Computer Science Ph.D. Dissertation