Donald Bren School of Information and Computer Sciences

Layered transmission of data is often recommended as a solution to the problem of varying bandwidth constraints in multicast applications. In the case of video multicast, this technique encodes multiple interdependent layers of video at arbitrary target rates in order to address heterogenous bandwidth constraints between the source and multiple receivers. However, multi-layered encoding alone is not sufficient to provide high video quality and high bandwidth utilization, because bandwidth constraints change over time. Adaptive techniques capable of adjusting the rates of video layers are required to maximize video quality and network utilization.

In this paper we define a class of algorithms known as Source-Adaptive Multi-layered Multicast (SAMM) algorithms. In SAMM algorithms, the source uses congestion feedback to adjust the number of generated layers and the bit rate of each layer. We contrast two specific SAMM algorithms: and end-to-end algorithm, in which only end systems monitor available bandwidth and report the amount of available bandwidth to the source, and a network-based algorithm, in which intermediate nodes also monitor and report available bandwidth. Using simulations which incorporate actual multi-layered video codecs, we demonstrate that SAMM algorithms exhibit better scalability and responsiveness to congestion than algorithms which are not source-adaptive. We also study the performance trade-offs between end-to-end and network-based SAMM algorithms.