A Video On Demand System Architecture For Heterogeneous Mobile Ad Hoc Networks For Different Devices.

this paper proposed new system architecture for Mobile Ad Hoc Networks (MANETs) on heterogeneous network to provide optimal Video on Demand (VoD) services to difference types of devices with optimal bandwidth utilization

A Scalable Solution For Interactive Video Streaming

This dissertation presents an overall solution for interactive Near Video On Demand (NVOD) systems, where limited server and network resources prevent the system from servicing all customers’ requests. The interactive nature of recent workloads complicates matters further. Interactive requests require additional resources to be handled. This dissertation analyzes the system performance under a realistic workload using different stream merging techniques and scheduling policies. It considers a wide range of system parameters and studies their impact on the waiting and blocking metrics. In order to improve waiting customers experience, we propose a new scheduling policy for waiting customers that is fairer and delivers a descent performance. Blocking is a major issue in interactive NVOD systems and we propose a few techniques to minimize it. In particular, we study the maximum Interactive Stream (I-Stream) length (Threshold) that should be allowed in order to prevent a few requests from using the expensive I-Streams for a prolonged period of time, which starves other requests from a chance of using this valuable resource. Using a reasonable I-Stream threshold proves very effective in improving blocking metrics. Moreover, we introduce an I-Stream provisioning policy to dynamically shift resources based on the system requirements at the time. The proposed policy proves to be highly effective in improving the overall system performance. To account for both average waiting time and average blocking time, we introduce a new metric (Aggregate Delay) . We study the client-side cache management policy. We utilize the customer’s cache to service most interactive requests, which reduces the load on the server. We propose three purging algorithms to clear data when the cache gets full. Purge Oldest removes the oldest data in the cache, whereas Purge Furthest clears the furthest data from the client’s playback point. In contrast, Adaptive Purge tries to avoid purging any data that includes the customer’s playback point or the playback point of any stream that is being listened to by the client. Additionally, we study the impact of the purge block, which is the least amount of data to be cleared, on the system performance. Finally, we study the effect of bookmarking on the system performance. A video segment that is searched and watched repeatedly is called a hotspot and is pointed to by a bookmark. We introduce three enhancements to effectively support bookmarking. Specifically, we propose a new purging algorithm to avoid purging hotspot data if it is already cached. On top of that, we fetch hotspot data for customers not listening to any stream. Furthermore, we reserve multicast channels to fetch hotspot data

Enabling Large-Scale Peer-to-Peer Stored Video Streaming Service with QoS Support

This research aims to enable a large-scale, high-volume, peer-to-peer, stored-video streaming service over the Internet, such as on-line DVD rentals. P2P allows a group of dynamically organized users to cooperatively support content discovery and distribution services without needing to employ a central server. P2P has the potential to overcome the scalability issue associated with client-server based video distribution networks; however, it brings a new set of challenges. This research addresses the following five technical challenges associated with the distribution of streaming video over the P2P network: 1) allow users with limited transmit bandwidth capacity to become contributing sources, 2) support the advertisement and discovery of time-changing and time-bounded video frame availability, 3) Minimize the impact of distribution source losses during video playback, 4) incorporate user mobility information in the selection of distribution sources, and 5) design a streaming network architecture that enables above functionalities.To meet the above requirements, we propose a video distribution network model based on a hybrid architecture between client-server and P2P. In this model, a video is divided into a sequence of small segments and each user executes a scheduling algorithm to determine the order, the timing, and the rate of segment retrievals from other users. The model also employs an advertisement and discovery scheme which incorporates parameters of the scheduling algorithm to allow users to share their life-time of video segment availability information in one advertisement and one query. An accompanying QoS scheme allows reduction in the number of video playback interruptions while one or more distribution sources depart from the service prematurely.The simulation study shows that the proposed model and associated schemes greatly alleviate the bandwidth requirement of the video distribution server, especially when the number of participating users grows large. As much as 90% of load reduction was observed in some experiments when compared to a traditional client-server based video distribution service. A significant reduction is also observed in the number of video presentation interruptions when the proposed QoS scheme is incorporated in the distribution process while certain percentages of distribution sources depart from the service unexpectedly

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

Maximizing Resource Utilization In Video Streaming Systems

Video streaming has recently grown dramatically in popularity over the Internet, Cable TV, and wire-less networks. Because of the resource demanding nature of video streaming applications, maximizing resource utilization in any video streaming system is a key factor to increase the scalability and decrease the cost of the system. Resources to utilize include server bandwidth, network bandwidth, battery life in battery operated devices, and processing time in limited processing power devices. In this work, we propose new techniques to maximize the utilization of video-on-demand (VOD) server resources. In addition to that, we propose new framework to maximize the utilization of the network bandwidth in wireless video streaming systems. Providing video streaming users in a VOD system with expected waiting times enhances their perceived quality-of-service (QoS) and encourages them to wait thereby increasing server utilization by increasing server throughput. In this work, we analyze waiting-time predictability in scalable video streaming. We also propose two prediction schemes and study their effectiveness when applied with various stream merging techniques and scheduling policies. The results demonstrate that the waiting time can be predicted accurately, especially when enhanced cost-based scheduling is applied. The combination of waiting-time prediction and cost-based scheduling leads to outstanding performance benefits. The achieved resource sharing by stream merging depends greatly on how the waiting requests are scheduled for service. Motivated by the development of cost-based scheduling, we investigate its effectiveness in great detail and discuss opportunities for further tunings and enhancements. Additionally, we analyze the effectiveness of incorporating video prediction results into the scheduling decisions. We also study the interaction between scheduling policies and the stream merging techniques and explore new ways for enhancements. The interest in video surveillance systems has grown dramatically during the last decade. Auto-mated video surveillance (AVS) serves as an efficient approach for the realtime detection of threats and for monitoring their progress. Wireless networks in AVS systems have limited available bandwidth that have to be estimated accurately and distributed efficiently. In this research, we develop two cross-layer optimization frameworks that maximize the bandwidth optimization of 802.11 wireless network. We develop a distortion-based cross-layer optimization framework that manages bandwidth in the wire-less network in such a way that minimizes the overall distortion. We also develop an accuracy-based cross-layer optimization framework in which the overall detection accuracy of the computer vision algorithm(s) running in the system is maximized. Both proposed frameworks manage the application rates and transmission opportunities of various video sources based on the dynamic network conditions to achieve their goals. Each framework utilizes a novel online approach for estimating the effective airtime of the network. Moreover, we propose a bandwidth pruning mechanism that can be used with the accuracy-based framework to achieve any desired tradeoff between detection accuracy and power consumption. We demonstrate the effectiveness of the proposed frameworks, including the effective air-time estimation algorithms and the bandwidth pruning mechanism, through extensive experiments using OPNET

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

Interactivity And User-heterogeneity In On Demand Broadcast Video

Video-On-Demand (VOD) has appeared as an important technology for many multimedia applications such as news on demand, digital libraries, home entertainment, and distance learning. In its simplest form, delivery of a video stream requires a dedicated channel for each video session. This scheme is very expensive and non-scalable. To preserve server bandwidth, many users can share a channel using multicast. Two types of multicast have been considered. In a non-periodic multicast setting, users make video requests to the server; and it serves them according to some scheduling policy. In a periodic broadcast environment, the server does not wait for service requests. It broadcasts a video cyclically, e.g., a new stream of the same video is started every t seconds. Although, this type of approach does not guarantee true VOD, the worst service latency experienced by any client is less than t seconds. A distinct advantage of this approach is that it can serve a very large community of users using minimal server bandwidth. In VOD System it is desirable to provide the user with the video-cassette-recorder-like (VCR) capabilities such as fast-forwarding a video or jumping to a specific frame. This issue in the broadcast framework is addressed, where each video and its interactive version are broadcast repeatedly on the network. Existing techniques rely on data prefetching as the mechanism to provide this functionality. This approach provides limited usability since the prefetching rate cannot keep up with typical fast-forward speeds. In the same environment, end users might have access to different bandwidth capabilities at different times. Current periodic broadcast schemes, do not take advantage of high-bandwidth capabilities, nor do they adapt to the low-bandwidth limitation of the receivers. A heterogeneous technique is presented that can adapt to a range of receiving bandwidth capability. Given a server bandwidth and a range of different client bandwidths, users employing the proposed technique will choose either to use their full reception bandwidth capability and therefore accessing the video at a very short time, or using part or enough reception bandwidth at the expense of a longer access latency