The Analysis and Performance of Multi-Rate Service in Distributed Video-on-Demand Systems

In this paper, we develop an end-to-end analysis of a distributed Video-on-Demand (VoD) system that includes an integrated model of the server and the network subsystems with analysis of its impact on client operations. The VoD system provides service to a heterogeneous client base at multiple playback rates. A class-based service model is developed where an incoming video request can specify a playback rate at which the data is consumed on the client. Using an analytical model, admission control conditions at the server and the network are derived for multi-rate service. We also develop client buffer requirements in presence of network delay bounds and delay jitter bounds using the same integrated framework of server and network subsystems. Results from an extensive simulation show that request handling policies based on limited redirection of blocked requests to other resources perform better than load sharing policies. The results also show that downgrading the service for blocked requests to a lower bitrate improves VoD system performance considerably. Combining the downgrade option with restrictions on access to high bitrate request classes is a powerful tool for manipulating an incoming request mix into a workload that the VoD system can handle.     

End-to-end analysis of distributed video-on-demand systems

The focus of the research presented in this paper is the end-to-end analysis of a distributed Video-on-Demand (VoD) system. We analyze the distributed architecture of a VoD system to design global request handling and admission control strategies and evaluate them using global metrics. The performance evaluation methodology developed in this paper helps in determining efficient ways of using all resources in the VoD architecture within the constraints of providing guaranteed high quality service to each request. For instance, our simulation results show that request handling policies based on limited redirection of blocked requests to other resources perform better than load sharing policies. We also show that request handling policies based on redirection have simpler connection establishment semantics than load sharing policies and, therefore, are easily incorporated into reservation or signaling protocols.     

Routing in intermittent networks using storage domains

In this paper, we present a routing algorithm for a class of networks where a contemporaneous end‐to‐end path may not exist at the time of data transfer due to intermittent links. Several examples of such networks exist in the context of sensor networks, mobile ad hoc networks and delay tolerant networks. The proposed routing algorithms follow a priori routing similar to source routing. Link state changes are assumed to be known ahead of time, for instance, due to planned duty cycling resulting in scheduled connectivity. The basic idea behind the proposed routing algorithms is to modify the breadth first search (BFS) algorithm to take into account link state changes and find the quickest route between source and destination nodes. We introduce the idea of time‐varying storage domains where all nodes connected for a length of time act as a single storage unit by sharing the aggregated storage capacity of the nodes. This will help situations where storage is a limited resource. We evaluate the routing algorithm with and without storage domain in an extensive simulation. The delay performance of the proposed algorithms is conceptually the same as flooding‐based algorithms but without the penalty of multiple copies. More significantly, we show that the Quickest Storage Domain (Quickest SD) algorithm distributes the storage demand across many nodes in the network topology, enabling balanced load and higher network utilization. In fact, we show that for the same level of performance, we can actually cut the storage requirement in half using the Quickest SD algorithm. Copyright © 2009 John Wiley & Sons, Ltd.     

Keyframe-based video summarization using Delaunay clustering

Recent advances in technology have made tremendous amounts of multimedia information available to the general population. An efficient way of dealing with this new development is to develop browsing tools that distill multimedia data as information oriented summaries. Such an approach will not only suit resource poor environments such as wireless and mobile, but also enhance browsing on the wired side for applications like digital libraries and repositories. Automatic summarization and indexing techniques will give users an opportunity to browse and select multimedia document of their choice for complete viewing later. In this paper, we present a technique by which we can automatically gather the frames of interest in a video for purposes of summarization. Our proposed technique is based on using Delaunay Triangulation for clustering the frames in videos. We represent the frame contents as multi-dimensional point data and use Delaunay Triangulation for clustering them. We propose a novel video summarization technique by using Delaunay clusters that generates good quality summaries with fewer frames and less redundancy when compared to other schemes. In contrast to many of the other clustering techniques, the Delaunay clustering algorithm is fully automatic with no user specified parameters and is well suited for batch processing. We demonstrate these and other desirable properties of the proposed algorithm by testing it on a collection of videos from Open Video Project. We provide a meaningful comparison between results of the proposed summarization technique with Open Video storyboard and K-means clustering. We evaluate the results in terms of metrics that measure the content representational value of the proposed technique.