A P2P Platform for real-time multicast video streaming leveraging on scalable multiple descriptions to cope with bandwidth fluctuations

In the immediate future video distribution applications will increase their diffusion thanks tothe ever-increasing user capabilities and improvements in the Internet access speed and performance.The target of this paper is to propose a content delivery system for real-time streaming services based ona peer-to-peer approach that exploits multicast overlay organization of the peers to address thechallenges due to bandwidth heterogeneity. To improve reliability and flexibility, video is coded using ascalable multiple description approach that allows delivery of sub-streams over multiple trees andallows rate adaptation along the trees as the available bandwidth changes. Moreover, we have deployeda new algorithm for tree-based topology management of the overlay network. In fact, tree based overlaynetworks better perform in terms of end-to-end delay and ordered delivery of video flow packets withrespect to mesh based ones. We also show with a case study that the proposed system works better thansimilar systems using only either multicast or multiple trees

Multiple description image and video coding for P2P transmissions

Peer-to-Peer (P2P) media streaming is, nowadays, a very attractive topic due to the bandwidth available to serve demanding content scales. A key challenge, however, is making content distribution robust to peer transience. Multiple description coding (MDC) has, indeed, proven to be very effective with problems concerning the packets’ losses, since it generates several descriptions and may reconstruct the original information with any number of descriptions that may reach the decoder. Therefore multiple descriptions may be effective for robust peer-to-peer media streaming. In this dissertation, it will not only be showed that, but also that varying the redundancy level of description on the fly may lead to a better performance than the one obtained without varying this parameter. Besides that, it is shown, as well, that varying the Bitrate on the fly outperforms the redundancy on it. Furthermore, the redundancy and the Bitrate were varied simultaneously. Thus, it is shown that this variation is more efficient when the packet loss is high. The experiments reported above were done using an experimental test bed developed for this purpose at the NMCG lab of the University of Beira Interior. It was also used the REGPROT, a video encoder developed by our research team, to splitted the video into multiple descriptions, which were, later, distributed among the peers in the test bed. After the request of the client, the referred encoder decoded the descriptions as they were being received.Fundação para a Ciência e a Tecnologia (FCT

Peer-to-peer live video streaming with rateless codes for massively multiplayer online games

International audienc

Optimizing on-demand resource deployment for peer-assisted content delivery (PhD thesis)

Increasingly, content delivery solutions leverage client resources in exchange for service in a peer-to-peer (P2P) fashion. Such peer-assisted service paradigms promise significant infrastructure cost reduction, but suffer from the unpredictability associated with client resources, which is often exhibited as an imbalance between the contribution and consumption of resources by clients. This imbalance hinders the ability to guarantee a minimum service fidelity of these services to the clients. In this thesis, we propose a novel architectural service model that enables the establishment of higher fidelity services through (1) coordinating the content delivery to optimally utilize the available resources, and (2) leasing the least additional cloud resources, available through special nodes (angels) that join the service on-demand, and only if needed, to complement the scarce resources available through clients. While the proposed service model can be deployed in many settings, this thesis focuses on peer-assisted content delivery applications, in which the scarce resource is typically the uplink capacity of clients. We target three applications that require the delivery of fresh as opposed to stale content. The first application is bulk-synchronous transfer, in which the goal of the system is to minimize the maximum distribution time -- the time it takes to deliver the content to all clients in a group. The second application is live streaming, in which the goal of the system is to maintain a given streaming quality. The third application is Tor, the anonymous onion routing network, in which the goal of the system is to boost performance (increase throughput and reduce latency) throughout the network, and especially for bandwidth-intensive applications. For each of the above applications, we develop mathematical models that optimally allocate the already available resources. They also optimally allocate additional on-demand resource to achieve a certain level of service. Our analytical models and efficient constructions depend on some simplifying, yet impractical, assumptions. Thus, inspired by our models and constructions, we develop practical techniques that we incorporate into prototypical peer-assisted angel-enabled cloud services. We evaluate those techniques through simulation and/or implementation. (Major Advisor: Azer Bestavros

Optimizing on-demand resource deployment for peer-assisted content delivery

Increasingly, content delivery solutions leverage client resources in exchange for services in a pee-to-peer (P2P) fashion. Such peer-assisted service paradigm promises significant infrastructure cost reduction, but suffers from the unpredictability associated with client resources, which is often exhibited as an imbalance between the contribution and consumption of resources by clients. This imbalance hinders the ability to guarantee a minimum service fidelity of these services to clients especially for real-time applications where content can not be cached. In this thesis, we propose a novel architectural service model that enables the establishment of higher fidelity services through (1) coordinating the content delivery to efficiently utilize the available resources, and (2) leasing the least additional cloud resources, available through special nodes (angels) that join the service on-demand, and only if needed, to complement the scarce resources available through clients. While the proposed service model can be deployed in many settings, this thesis focuses on peer-assisted content delivery applications, in which the scarce resource is typically the upstream capacity of clients. We target three applications that require the delivery of real-time as opposed to stale content. The first application is bulk-synchronous transfer, in which the goal of the system is to minimize the maximum distribution time - the time it takes to deliver the content to all clients in a group. The second application is live video streaming, in which the goal of the system is to maintain a given streaming quality. The third application is Tor, the anonymous onion routing network, in which the goal of the system is to boost performance (increase throughput and reduce latency) throughout the network, and especially for clients running bandwidth-intensive applications. For each of the above applications, we develop analytical models that efficiently allocate the already available resources. They also efficiently allocate additional on-demand resource to achieve a certain level of service. Our analytical models and efficient constructions depend on some simplifying, yet impractical, assumptions. Thus, inspired by our models and constructions, we develop practical techniques that we incorporate into prototypical peer-assisted angel-enabled cloud services. We evaluate these techniques through simulation and/or implementation

Investigating the Effects of Network Dynamics on Quality of Delivery Prediction and Monitoring for Video Delivery Networks

Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams. This PhD investigates the effects of network dynamics in QoD prediction using ML techniques. The hypothesis that this thesis investigates is that ML techniques that model the underlying network dynamics achieve accurate QoD and video quality predictions and measurements. The thesis results demonstrate that the proposed techniques offer performance gains over approaches that fail to consider network dynamics. This thesis results highlight that adopting the correct model by modelling the dynamics of the network infrastructure is crucial to the accuracy of the ML predictions. These results are significant as they demonstrate that improved performance is achieved at no additional computational or storage cost. These techniques can help the network manager, data center operatives and video service providers take proactive and corrective actions for improved network efficiency and effectiveness

A Framework For Efficient Data Distribution In Peer-to-peer Networks.

Peer to Peer (P2P) models are based on user altruism, wherein a user shares its content with other users in the pool and it also has an interest in the content of the other nodes. Most P2P systems in their current form are not fair in terms of the content served by a peer and the service obtained from swarm. Most systems suffer from free rider\u27s problem where many high uplink capacity peers contribute much more than they should while many others get a free ride for downloading the content. This leaves high capacity nodes with very little or no motivation to contribute. Many times such resourceful nodes exit the swarm or don\u27t even participate. The whole scenario is unfavorable and disappointing for P2P networks in general, where participation is a must and a very important feature. As the number of users increases in the swarm, the swarm becomes robust and scalable. Other important issues in the present day P2P system are below optimal Quality of Service (QoS) in terms of download time, end-to-end latency and jitter rate, uplink utilization, excessive cross ISP traffic, security and cheating threats etc. These current day problems in P2P networks serve as a motivation for present work. To this end, we present an efficient data distribution framework in Peer-to-Peer (P2P) networks for media streaming and file sharing domain. The experiments with our model, an alliance based peering scheme for media streaming, show that such a scheme distributes data to the swarm members in a near-optimal way. Alliances are small groups of nodes that share data and other vital information for symbiotic association. We show that alliance formation is a loosely coupled and an effective way to organize the peers and our model maps to a small world network, which form efficient overlay structures and are robust to network perturbations such as churn. We present a comparative simulation based study of our model with CoolStreaming/DONet (a popular model) and present a quantitative performance evaluation. Simulation results show that our model scales well under varying workloads and conditions, delivers near optimal levels of QoS, reduces cross ISP traffic considerably and for most cases, performs at par or even better than Cool-Streaming/DONet. In the next phase of our work, we focussed on BitTorrent P2P model as it the most widely used file sharing protocol. Many studies in academia and industry have shown that though BitTorrent scales very well but is far from optimal in terms of fairness to end users, download time and uplink utilization. Furthermore, random peering and data distribution in such model lead to suboptimal performance. Lately, new breed of BitTorrent clients like BitTyrant have shown successful strategic attacks against BitTorrent. Strategic peers configure the BitTorrent client software such that for very less or no contribution, they can obtain good download speeds. Such strategic nodes exploit the altruism in the swarm and consume resources at the expense of other honest nodes and create an unfair swarm. More unfairness is generated in the swarm with the presence of heterogeneous bandwidth nodes. We investigate and propose a new token-based anti-strategic policy that could be used in BitTorrent to minimize the free-riding by strategic clients. We also proposed other policies against strategic attacks that include using a smart tracker that denies the request of strategic clients for peer listmultiple times, and black listing the non-behaving nodes that do not follow the protocol policies. These policies help to stop the strategic behavior of peers to a large extent and improve overall system performance. We also quantify and validate the benefits of using bandwidth peer matching policy. Our simulations results show that with the above proposed changes, uplink utilization and mean download time in BitTorrent network improves considerably. It leaves strategic clients with little or no incentive to behave greedily. This reduces free riding and creates fairer swarm with very little computational overhead. Finally, we show that our model is self healing model where user behavior changes from selfish to altruistic in the presence of the aforementioned policies

Peer-to-peer multimedia communication

I sistemi Peer-to-Peer (P2P) sono stati inventi, messi in campo e studiati da più di dieci anni, andando al di là della semplice applicazione per scambio di file. Nelle reti P2P i partecipanti si organizzano in una rete "overlay" che è astratta rispetto alle caratteristiche della sottostante rete fisica. Scopo di questi sistemi è la distribuzione di risorse quali contenuti, spazio di memorizzazione o cicli macchina. Gli utenti quindi giocano un ruolo attivo e possono essere considerati come sia clienti che serventi allo stesso tempo per il particolare servizio che la rete P2P offre. Lo scopo di questa tesi di dottorato è lo studio di questi sistemi ed il dare un contributo nella loro analisi prestazionale. L'analisi mira a valutare le prestazioni raggiunte dai sistemi e/o i limiti teorici raggiungibili. Infatti, nonostante esistano diversi meccanismi per il peer-to-peer streaming, l'analisi prestazionale di questo tipo di sistemi può essere considerata ancora nella sua infanzia. A questo scopo, i contributi principali di questa tesi di dottorato sono: i)la derivazione di un limite teorico per il ritardo nei sistemi di P2P streaming, ii) la creazione di un algoritmo che sfrutti le conoscenze acquisite attraverso il lavoro teorico, iii) l'analisi prestazionale dell'algoritmo utilizzando un simulatore espressamente progettato per riprodurre le caratteristiche delle reti P2P reali composte da centinaia di migliaia di nodi che si connettono e disconnettono in continuazione.Peer-to-Peer (P2P) systems have been invented, deployed and researched for more than ten years and went far beyond the simple file sharing applications. In P2P networks, participants organize themselves in an overlay network that abstracts from the topological characteristics of the underlying physical network. Aim of these systems is the distribution of some kind of resources like contents, storage, or CPU cycles. Users, therefore, play an active role so that they can be considered as client and server at the same time, for the particular service that is provided through the P2P paradigm. Goal of this dissertation thesis is to study these systems, and give contributes in their performance evaluation. The analysis will aim to evaluate the achieved performance of a system and/or the performance bounds that could be achievable. In fact, even if there are several proposals of different systems, peer-to-peer streaming performance analysis can be considered still in its infancy and there is still a lot of work to do. To this aim, the main contributes of this dissertation thesis are i) the derivation of a theoretical delay bounds for P2P streaming system ii) II the creation of an algorithm that exploits the new insights that come out from the theoretical study iii) the performance evaluation of this algorithm using an ad-hoc simulator, expressly tailored to reproduce the characteristics of the real-world P2P streaming systems, composed by hundred thousands of intermittently connected users

Towards video streaming in IoT environments: vehicular communication perspective

Multimedia oriented Internet of Things (IoT) enables pervasive and real-time communication of video, audio and image data among devices in an immediate surroundings. Today's vehicles have the capability of supporting real time multimedia acquisition. Vehicles with high illuminating infrared cameras and customized sensors can communicate with other on-road devices using dedicated short-range communication (DSRC) and 5G enabled communication technologies. Real time incidence of both urban and highway vehicular traffic environment can be captured and transmitted using vehicle-to-vehicle and vehicle-to-infrastructure communication modes. Video streaming in vehicular IoT (VSV-IoT) environments is in growing stage with several challenges that need to be addressed ranging from limited resources in IoT devices, intermittent connection in vehicular networks, heterogeneous devices, dynamism and scalability in video encoding, bandwidth underutilization in video delivery, and attaining application-precise quality of service in video streaming. In this context, this paper presents a comprehensive review on video streaming in IoT environments focusing on vehicular communication perspective. Specifically, significance of video streaming in vehicular IoT environments is highlighted focusing on integration of vehicular communication with 5G enabled IoT technologies, and smart city oriented application areas for VSV-IoT. A taxonomy is presented for the classification of related literature on video streaming in vehicular network environments. Following the taxonomy, critical review of literature is performed focusing on major functional model, strengths and weaknesses. Metrics for video streaming in vehicular IoT environments are derived and comparatively analyzed in terms of their usage and evaluation capabilities. Open research challenges in VSV-IoT are identified as future directions of research in the area. The survey would benefit both IoT and vehicle industry practitioners and researchers, in terms of augmenting understanding of vehicular video streaming and its IoT related trends and issues