Optimal scheduling for streaming of scalable media
Scalable, or layered, media representation appears to be more suitable for transmission over the current heterogeneous networks. In this paper we study the problem of scalable layered streaming media delivery over a lossy channel. The goal is to find an optimal transmission policy to achieve the best playback quality at the client end. The problem involves some trade-offs such as time-constrained delivery and data dependencies. For example, a layer should be dropped before transmission if it already has a delay such that it cannot be played before its scheduled time. Moreover, less important layers with near-playback-time may also be dropped or delayed for delivery in order to save bandwidth for other layers with a high priority. We propose a framework for scalable streaming media delivery, that involves a novel scheduling algorithm called Expected runtime Distortion Based Scheduling (EDBS) which decides the order in which packets should be transmitted in order to improve client playback quality in the presence of channel losses. A fast greedy search algorithm is presented that achieves almost the same performance as an exhaustive search technique (98% of the time it results in the same schedule) with very low complexity and is applicable for real-time application.