Optimal Universal Schedules for Discrete Broadcast

We study broadcast systems that distribute a series of data updates to a large number of passive clients. The updates are sent over a broadcast channel in the form of discrete packets. We assume that clients periodically access the channel to obtain the most recent update. Such scenarios arise in many practical applications, such as distribution of traffic information and market updates to mobile wireless devices

Providing VCR Functionality in VOD Servers

Resource-sharing techniques are widely used by VOD servers. Stream merging is one of the most efficient resource-sharing techniques. ERMT is able to achieve merge trees with the closest cost of optimal merge tree. Full VCR support has become a “must have” feature for VOD services. This researcher proposed an algorithm to enable VCR support on ERMT. Furthermore, client local buffer and fixed-interval periodical multicasting were also deployed by the algorithm to improve the stream-client ratio. After thorough runs of simulations and numerous comparisons to BEP, the highly efficient resource- sharing technique, the proposed algorithm with client local buffer utilization and fixed- interval multicasting showed better performance in all simulations. The biggest discovery is that the best-performer is modified ERMT with client local buffer support for VCR without fixed-interval multicasting. Another discovery is that bigger client buffer size hurts the performance of ERMT

Scalable on-demand streaming of stored complex multimedia

Previous research has developed a number of efficient protocols for streaming popular multimedia files on-demand to potentially large numbers of concurrent clients. These protocols can achieve server bandwidth usage that grows much slower than linearly with the file request rate, and with the inverse of client start-up delay. This hesis makes the following three main contributions to the design and performance evaluation of such protocols. The first contribution is an investigation of the network bandwidth requirements for scalable on-demand streaming. The results suggest that the minimum required network bandwidth for scalable on-demand streaming typically scales as K/ln(K) as the number of client sites K increases for fixed request rate per client site, and as ln(N/(ND+1)) as the total file request rate N increases or client start-up delay D decreases, for a fixed number of sites. Multicast delivery trees configured to minimize network bandwidth usage rather than latency are found to only modestly reduce the minimum required network bandwidth. Furthermore, it is possible to achieve close to the minimum possible network and server bandwidth usage simultaneously with practical scalable delivery protocols. Second, the thesis addresses the problem of scalable on-demand streaming of a more complex type of media than is typically considered, namely variable bit rate (VBR) media. A lower bound on the minimum required server bandwidth for scalable on-demand streaming of VBR media is derived. The lower bound analysis motivates the design of a new immediate service protocol termed VBR bandwidth skimming (VBRBS) that uses constant bit rate streaming, when sufficient client storage space is available, yet fruitfully exploits the knowledge of a VBR profile. Finally, the thesis proposes non-linear media containing parallel sequences of data frames, among which clients can dynamically select at designated branch points, and investigates the design and performance issues in scalable on-demand streaming of such media. Lower bounds on the minimum required server bandwidth for various non-linear media scalable on-demand streaming approaches are derived, practical non-linear media scalable delivery protocols are developed, and, as a proof-of-concept, a simple scalable delivery protocol is implemented in a non-linear media streaming prototype system