Algorithms for streaming, caching and storage of digital media

Streaming media applications have become the important components in multimedia communications. Typically, these applications require real-time data delivery in order to provide continuous playback with good visual quality. However, the real-time constraints may not be explicitly guaranteed when the streaming media is delivered over networks exhibiting time varying behavior. Streaming systems are designed to maximize the playback quality in the presence of various channel conditions. This research includes studies of different components of a streaming system, and proposes several algorithms to improve the streaming performances.; The first part of this thesis considers the transport of scalable streaming media over best-effort networks (e.g., today's Internet) and proposes a scheduling algorithm for packet delivery. The proposed algorithm first determines the importance values of all packets in the transmission buffer, based on the packet contents, channel conditions and client feedbacks. Then the algorithm guides the media server to transmit more important data packets earlier than others, and therefore improves the client playback quality.; The second part focuses on video caching, and shows that the streaming performance can be improved even when only part of the video object is cached in the proxy. Two video caching algorithms are proposed to store selected frames of the video in the proxy, under the constraints of cache space and decoder buffer size. The first caching algorithm aims at reducing the cost for channel bandwidth reservation in QoS networks; while the second one is designed for best-effort networks, with the goal to improve the robustness of continuous playback against poor channel conditions (e.g., packet delay and loss).; The last part of this thesis addresses the video compression problem combined with the disk storage strategies for video servers. Video disk storage algorithms aim at improving the video server throughput by placing the video data blocks in a special order (therefore reducing the disk seek time). We translate the specified disk placement algorithm into rate constraints for video compression, and propose a rate-distortion based compression algorithm to improve the video quality, while maximizing the advantage achieved with the disk placement strategies.