Exploiting Traffic Balancing and Multicast Efficiency in Distributed Video-on-Demand Architectures

Distributed Video-on-Demand (DVoD) systems are proposed as a solution to the limited streaming capacity and null scalability of centralized systems. In a previous work, we proposed a fully distributed large-scale VoD architecture, called Double P-Tree, which has shown itself to be a good approach to the design of flexible and scalable DVoD systems. In this paper, we present relevant design aspects related to video mapping and traffic balancing in order to improve Double P-Tree architecture performance. Our simulation results demonstrate that these techniques yield a more efficient system and considerably increase its streaming capacity. The results also show the crucial importance of topology connectivity in improving multicasting performance in DVoD systems. Finally, a comparison among several DVoD architectures was performed using simulation, and the results show that the Double P-Tree architecture incorporating mapping and load balancing policies outperforms similar DVoD architectures.This work was supported by the MCyT-Spain under contract TIC 2001-2592 and partially supported by the Generalitat de Catalunya- Grup de Recerca Consolidat 2001SGR-00218

Study on Large-scale Terrestrial Relaying of Satellite Broadcasted Real-time Multimedia Streams

none2The chapter describes an architecture to relay on demand a real-time IP multicast audio-video stream broadcasted by a satellite on a terrestrial link. The stream is received by suitably equipped sites and then relayed to other sites that are not equipped with satellite receiving hardware but are nonetheless willing to receive the stream. By exploiting the properties of satellite transmission and adopting a hybrid satellite/terrestrial, multicast/unicast approach, the described architecture allows to overcome the restrictions suffered by multicast traffic in the global Internet, allowing it to scale easily across autonomous systems. All things considered, the proposed architecture outlines a large-scale interactive audio-video distribution system similar to those based on Content Distribution Networks (CDNs) and it compares favourably with them when performances, costs and scalability are examined.openFranco Tommasi; Catiuscia MelleTommasi, Francesco; Melle, Catiusci

Modeling and Evaluation of Multisource Streaming Strategies in P2P VoD Systems

In recent years, multimedia content distribution has largely been moved to the Internet, inducing broadcasters, operators and service providers to upgrade with large expenses their infrastructures. In this context, streaming solutions that rely on user devices such as set-top boxes (STBs) to offload dedicated streaming servers are particularly appropriate. In these systems, contents are usually replicated and scattered over the network established by STBs placed at users' home, and the video-on-demand (VoD) service is provisioned through streaming sessions established among neighboring STBs following a Peer-to-Peer fashion. Up to now the majority of research works have focused on the design and optimization of content replicas mechanisms to minimize server costs. The optimization of replicas mechanisms has been typically performed either considering very crude system performance indicators or analyzing asymptotic behavior. In this work, instead, we propose an analytical model that complements previous works providing fairly accurate predictions of system performance (i.e., blocking probability). Our model turns out to be a highly scalable, flexible, and extensible tool that may be helpful both for designers and developers to efficiently predict the effect of system design choices in large scale STB-VoD system

Capacity of P2P on-demand streaming with simple, robust and decentralized control

The performance of large-scaled peer-to-peer (P2P) video-on-demand (VoD) streaming systems can be very challenging to analyze. In practical P2P VoD systems, each peer only interacts with a small number of other peers/neighbors. Further, its upload capacity may vary randomly, and both its downloading position and content availability change dynamically. In this paper, we rigorously study the achievable streaming capacity of large-scale P2P VoD systems with sparse connectivity among peers, and investigate simple and decentralized P2P control strategies that can provably achieve close-to-optimal streaming capacity. We first focus on a single streaming channel. We show that a close-to-optimal streaming rate can be asymptotically achieved for all peers with high probability as the number of peers N increases, by assigning each peer a random set of Θ(log N) neighbors and using a uniform rate-allocation algorithm. Further, the tracker does not need to obtain detailed knowledge of which chunks each peer caches, and hence incurs low overhead. We then study multiple streaming channels where peers watching one channel may help in another channel with insufficient upload bandwidth. We propose a simple random cache-placement strategy, and show that a close-to-optimal streaming capacity region for all channels can be attained with high probability, again with only Θ(logN) per-peer neighbors. These results provide important insights into the dynamics of large-scale P2P VoD systems, which will be useful for guiding the design of improved P2P control protocols. © 2013 IEEE.published_or_final_versio

A Novel Clustering Tree-based Video lookup Strategy for Supporting VCR-like Operations in MANETs

Mobile Peer-to-Peer (MP2P) network is a promising avenue for large-scale deployment of Video-on-Demand (VoD) applications over mobile ad-hoc networks (MANETs). In P2P VoD systems, fast search for resources is key determinants for improving the Quality of Service (QoS) due to the low delay of seeking resources caused by streaming interactivity. In this paper, we propose a novel Clustering Tree-based Video Lookup strategy for supporting VCR-like operations in MANETs (CTVL) CTVL selects the chunks with the high popularity as "overlay router" chunks to build the "virtual connection" with other chunks in terms of the popularities and external connection of video chunks. CTVL designs a new clustering strategy to group nodes in P2P networks and a maintenance mechanism of cluster structure, which achieves the high system scalability and fast resource search performance. Thorough simulation results also show how CTVL achieves higher average lookup success rate, lower maintenance cost, lower average end-to-end delay and lower packet loss ratio (PLR) in comparison with other state of the art solutions

Video trajectory analysis using unsupervised clustering and multi-criteria ranking

Surveillance camera usage has increased significantly for visual surveillance. Manual analysis of large video data recorded by cameras may not be feasible on a larger scale. In various applications, deep learning-guided supervised systems are used to track and identify unusual patterns. However, such systems depend on learning which may not be possible. Unsupervised methods relay on suitable features and demand cluster analysis by experts. In this paper, we propose an unsupervised trajectory clustering method referred to as t-Cluster. Our proposed method prepares indexes of object trajectories by fusing high-level interpretable features such as origin, destination, path, and deviation. Next, the clusters are fused using multi-criteria decision making and trajectories are ranked accordingly. The method is able to place abnormal patterns on the top of the list. We have evaluated our algorithm and compared it against competent baseline trajectory clustering methods applied to videos taken from publicly available benchmark datasets. We have obtained higher clustering accuracies on public datasets with significantly lesser computation overhead