Coded Caching for Delay-Sensitive Content

Coded caching is a recently proposed technique that achieves significant performance gains for cache networks compared to uncoded caching schemes. However, this substantial coding gain is attained at the cost of large delivery delay, which is not tolerable in delay-sensitive applications such as video streaming. In this paper, we identify and investigate the tradeoff between the performance gain of coded caching and the delivery delay. We propose a computationally efficient caching algorithm that provides the gains of coding and respects delay constraints. The proposed algorithm achieves the optimum performance for large delay, but still offers major gains for small delay. These gains are demonstrated in a practical setting with a video-streaming prototype.Comment: 9 page

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

Research of Proxy Cache Algorithm in Multi-media Education System

Multi-media education system is more and more widely used in all levels of education. In order to decrease cost of multi-media system and keep efficiency with increasing multi-media materials, proxy cache algorithm has been widely studied. Based on analysis of existing research of proxy cache results, an improved proxy coaching strategy of prefix cache and postfix merging is proposed. The strategy can dynamically adjust prefix cache size with the object access change. A more effective method of steaming merging has been proposed with multicast used in postfix portion. The results show that the improved strategy can effectively utilize proxy cache resource, shorten time delay and save band width

Design And Analysis Of Scalable Video Streaming Systems

Despite the advancement in multimedia streaming technology, many multimedia applications are still face major challenges, including provision of Quality-of-Service (QoS), system scalability, limited resources, and cost. In this dissertation, we develop and analyze a new set of metrics based on two particular video streaming systems, namely: (1) Video-on-Demand (VOD) with video advertisements system and (2) Automated Video Surveillance System (AVS). We address the main issues in the design of commercial VOD systems: scalability and support of video advertisements. We develop a scalable delivery framework for streaming media content with video advertisements. The delivery framework combines the benefits of stream merging and periodic broadcasting. In addition, we propose new scheduling policies that are well-suited for the proposed delivery framework. We also propose a new prediction scheme of the ad viewing times, called Assign Closest Ad Completion Time (ACA). Moreover, we propose an enhanced business model, in which the revenue generated from advertisements is used to subsidize the price. Additionally, we investigate the support of targeted advertisements, whereby clients receive ads that are well-suited for their interests and needs. Furthermore, we provide the clients with the ability to select from multiple price options, each with an associate expected number of viewed ads. We provide detailed analysis of the proposed VOD system, considering realistic workload and a wide range of design parameters. In the second system, Automated Video Surveillance (AVS), we consider the system design for optimizing the subjects recognition probabilities. We focus on the management and the control of various Pan, Tilt, Zoom (PTZ) video cameras. In particular, we develop a camera management solution that provides the best tradeoff between the subject recognition probability and time complexity. We consider both subject grouping and clustering mechanisms. In subject grouping, we propose the Grid Based Grouping (GBG) and the Elevator Based P lanning (EBP) algorithms. In the clustering approach, we propose the (GBG) with Clustering (GBGC) and the EBP with Clustering (EBPC) algorithms. We characterize the impact of various factors on recognition probability. These factors include resolution, pose and zoom-distance noise. We provide detailed analysis of the camera management solution, considering realistic workload and system design parameters

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

Shortest Path versus Multi-Hub Routing in Networks with Uncertain Demand

We study a class of robust network design problems motivated by the need to scale core networks to meet increasingly dynamic capacity demands. Past work has focused on designing the network to support all hose matrices (all matrices not exceeding marginal bounds at the nodes). This model may be too conservative if additional information on traffic patterns is available. Another extreme is the fixed demand model, where one designs the network to support peak point-to-point demands. We introduce a capped hose model to explore a broader range of traffic matrices which includes the above two as special cases. It is known that optimal designs for the hose model are always determined by single-hub routing, and for the fixed- demand model are based on shortest-path routing. We shed light on the wider space of capped hose matrices in order to see which traffic models are more shortest path-like as opposed to hub-like. To address the space in between, we use hierarchical multi-hub routing templates, a generalization of hub and tree routing. In particular, we show that by adding peak capacities into the hose model, the single-hub tree-routing template is no longer cost-effective. This initiates the study of a class of robust network design (RND) problems restricted to these templates. Our empirical analysis is based on a heuristic for this new hierarchical RND problem. We also propose that it is possible to define a routing indicator that accounts for the strengths of the marginals and peak demands and use this information to choose the appropriate routing template. We benchmark our approach against other well-known routing templates, using representative carrier networks and a variety of different capped hose traffic demands, parameterized by the relative importance of their marginals as opposed to their point-to-point peak demands