Video-on-Demand over Internet: a survey of existing systems and solutions

Video-on-Demand is a service where movies are delivered to distributed users with low delay and free interactivity. The traditional client/server architecture experiences scalability issues to provide video streaming services, so there have been many proposals of systems, mostly based on a peer-to-peer or on a hybrid server/peer-to-peer solution, to solve this issue. This work presents a survey of the currently existing or proposed systems and solutions, based upon a subset of representative systems, and defines selection criteria allowing to classify these systems. These criteria are based on common questions such as, for example, is it video-on-demand or live streaming, is the architecture based on content delivery network, peer-to-peer or both, is the delivery overlay tree-based or mesh-based, is the system push-based or pull-based, single-stream or multi-streams, does it use data coding, and how do the clients choose their peers. Representative systems are briefly described to give a summarized overview of the proposed solutions, and four ones are analyzed in details. Finally, it is attempted to evaluate the most promising solutions for future experiments. Résumé La vidéo à la demande est un service où des films sont fournis à distance aux utilisateurs avec u

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

Mathematical analysis of scheduling policies in peer-to-peer video streaming networks

Las redes de pares son comunidades virtuales autogestionadas, desarrolladas en la capa de aplicación sobre la infraestructura de Internet, donde los usuarios (denominados pares) comparten recursos (ancho de banda, memoria, procesamiento) para alcanzar un fin común. La distribución de video representa la aplicación más desafiante, dadas las limitaciones de ancho de banda. Existen básicamente tres servicios de video. El más simple es la descarga, donde un conjunto de servidores posee el contenido original, y los usuarios deben descargar completamente este contenido previo a su reproducción. Un segundo servicio se denomina video bajo demanda, donde los pares se unen a una red virtual siempre que inicien una solicitud de un contenido de video, e inician una descarga progresiva en línea. El último servicio es video en vivo, donde el contenido de video es generado, distribuido y visualizado simultáneamente. En esta tesis se estudian aspectos de diseño para la distribución de video en vivo y bajo demanda. Se presenta un análisis matemático de estabilidad y capacidad de arquitecturas de distribución bajo demanda híbridas, asistidas por pares. Los pares inician descargas concurrentes de múltiples contenidos, y se desconectan cuando lo desean. Se predice la evolución esperada del sistema asumiendo proceso Poisson de arribos y egresos exponenciales, mediante un modelo determinístico de fluidos. Un sub-modelo de descargas secuenciales (no simultáneas) es globalmente y estructuralmente estable, independientemente de los parámetros de la red. Mediante la Ley de Little se determina el tiempo medio de residencia de usuarios en un sistema bajo demanda secuencial estacionario. Se demuestra teóricamente que la filosofía híbrida de cooperación entre pares siempre desempeña mejor que la tecnología pura basada en cliente-servidor

Computer Science and Technology Series : XV Argentine Congress of Computer Science. Selected papers

CACIC'09 was the fifteenth Congress in the CACIC series. It was organized by the School of Engineering of the National University of Jujuy. The Congress included 9 Workshops with 130 accepted papers, 1 main Conference, 4 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses. CACIC 2009 was organized following the traditional Congress format, with 9 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of three chairs of different Universities. The call for papers attracted a total of 267 submissions. An average of 2.7 review reports were collected for each paper, for a grand total of 720 review reports that involved about 300 different reviewers. A total of 130 full papers were accepted and 20 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

Entrega de conteúdos multimédia em over-the-top: caso de estudo das gravações automáticas

Doutoramento em Engenharia EletrotécnicaOver-The-Top (OTT) multimedia delivery is a very appealing approach for providing ubiquitous, exible, and globally accessible services capable of low-cost and unrestrained device targeting. In spite of its appeal, the underlying delivery architecture must be carefully planned and optimized to maintain a high Qualityof- Experience (QoE) and rational resource usage, especially when migrating from services running on managed networks with established quality guarantees. To address the lack of holistic research works on OTT multimedia delivery systems, this Thesis focuses on an end-to-end optimization challenge, considering a migration use-case of a popular Catch-up TV service from managed IP Television (IPTV) networks to OTT. A global study is conducted on the importance of Catch-up TV and its impact in today's society, demonstrating the growing popularity of this time-shift service, its relevance in the multimedia landscape, and tness as an OTT migration use-case. Catch-up TV consumption logs are obtained from a Pay-TV operator's live production IPTV service containing over 1 million subscribers to characterize demand and extract insights from service utilization at a scale and scope not yet addressed in the literature. This characterization is used to build demand forecasting models relying on machine learning techniques to enable static and dynamic optimization of OTT multimedia delivery solutions, which are able to produce accurate bandwidth and storage requirements' forecasts, and may be used to achieve considerable power and cost savings whilst maintaining a high QoE. A novel caching algorithm, Most Popularly Used (MPU), is proposed, implemented, and shown to outperform established caching algorithms in both simulation and experimental scenarios. The need for accurate QoE measurements in OTT scenarios supporting HTTP Adaptive Streaming (HAS) motivates the creation of a new QoE model capable of taking into account the impact of key HAS aspects. By addressing the complete content delivery pipeline in the envisioned content-aware OTT Content Delivery Network (CDN), this Thesis demonstrates that signi cant improvements are possible in next-generation multimedia delivery solutions.A entrega de conteúdos multimédia em Over-The-Top (OTT) e uma proposta atractiva para fornecer um serviço flexível e globalmente acessível, capaz de alcançar qualquer dispositivo, com uma promessa de baixos custos. Apesar das suas vantagens, e necessario um planeamento arquitectural detalhado e optimizado para manter níveis elevados de Qualidade de Experiência (QoE), em particular aquando da migração dos serviços suportados em redes geridas com garantias de qualidade pré-estabelecidas. Para colmatar a falta de trabalhos de investigação na área de sistemas de entrega de conteúdos multimédia em OTT, esta Tese foca-se na optimização destas soluções como um todo, partindo do caso de uso de migração de um serviço popular de Gravações Automáticas suportado em redes de Televisão sobre IP (IPTV) geridas, para um cenário de entrega em OTT. Um estudo global para aferir a importância das Gravações Automáticas revela a sua relevância no panorama de serviços multimédia e a sua adequação enquanto caso de uso de migração para cenários OTT. São obtidos registos de consumos de um serviço de produção de Gravações Automáticas, representando mais de 1 milhão de assinantes, para caracterizar e extrair informação de consumos numa escala e âmbito não contemplados ate a data na literatura. Esta caracterização e utilizada para construir modelos de previsão de carga, tirando partido de sistemas de machine learning, que permitem optimizações estáticas e dinâmicas dos sistemas de entrega de conteúdos em OTT através de previsões das necessidades de largura de banda e armazenamento, potenciando ganhos significativos em consumo energético e custos. Um novo mecanismo de caching, Most Popularly Used (MPU), demonstra um desempenho superior as soluções de referencia, quer em cenários de simulação quer experimentais. A necessidade de medição exacta da QoE em streaming adaptativo HTTP motiva a criaçao de um modelo capaz de endereçar aspectos específicos destas tecnologias adaptativas. Ao endereçar a cadeia completa de entrega através de uma arquitectura consciente dos seus conteúdos, esta Tese demonstra que são possíveis melhorias de desempenho muito significativas nas redes de entregas de conteúdos em OTT de próxima geração

Computer Science and Technology Series : XV Argentine Congress of Computer Science. Selected papers

CACIC'09 was the fifteenth Congress in the CACIC series. It was organized by the School of Engineering of the National University of Jujuy. The Congress included 9 Workshops with 130 accepted papers, 1 main Conference, 4 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses. CACIC 2009 was organized following the traditional Congress format, with 9 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of three chairs of different Universities. The call for papers attracted a total of 267 submissions. An average of 2.7 review reports were collected for each paper, for a grand total of 720 review reports that involved about 300 different reviewers. A total of 130 full papers were accepted and 20 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

Design and optimization of QoS-based medium access control protocols for next-generation wireless LANs

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In recent years, there have been tremendous advances in wireless & mobile communications, including wireless radio techniques, networking protocols, and mobile devices. It is expected that different broadband wireless access technologies, e.g., WiFi (IEEE 802.11) and WiMAX (IEEE 802.16) will coexist in the future. In the meantime, multimedia applications have experienced an explosive growth with increasing user demands. Nowadays, people expect to receive high-speed video, audio, voice and web services even when being mobile. The key question that needs to be answered, then, is how do we ensure that users always have the "best" network performance with the "lowest" costs in such complicated situations? The latest IEEE 802.11n standards attains rates of more than 100 Mbps by introducing innovative enhancements at the PHY and MAC layer, e.g. MIMO and Frame Aggregation, respectively. However, in this thesis we demonstrate that frame aggregation's performance adheres due to the EDCA scheduler's priority mechanism and consequently resulting in the network's poor overall performance. Short waiting times for high priority flows into the aggregation queue resolves to poor channel utilization. A Delayed Channel Access algorithm was designed to intentionally postpone the channel access procedure so that the number of packets in a formed frame can be increased and so will the network's overall performance. However, in some cases, the DCA algorithm has a negative impact on the applications that utilize the TCP protocol, especially the when small TCP window sizes are engaged. So, the TCP process starts to refrain from sending data due to delayed acknowledgements and the overall throughput drops. In this thesis, we address the above issues by firstly demonstrating the potential performance benefits of frame aggregation over the next generation wireless networks. The efficiency and behaviour of frame aggregation within a single queue, are mathematically analysed with the aid of a M=G[a;b]=1=K model. Results show that a trade-off choice has to be taken into account over minimizing the waiting time or maximizing utilization. We also point out that there isn't an optimum batch collection rule which can be assumed as generally valid but individual cases have to be considered separately. Secondly, we demonstrate through extensive simulations that by introducing a method, the DCA algorithm, which dynamically determines and adapts batch collections based upon the traffic's characteristics, QoS requirements and server's maximum capacity, also improves e ciency. Thirdly, it is important to understand the behaviour of the TCP ows over the WLAN and the influence that DCA has over the degrading performance of the TCP protocol. We investigate the cause of the problem and provide the foundations of designing and implementing possible solutions. Fourthly, we introduce two innovative proposals, one amendment and one extension to the original DCA algorithm, called Adaptive DCA and Selective DCA, respectively. Both solutions have been implemented in OPNET and extensive simulation runs over a wide set of scenarios show their effectiveness over the network's overall performance, each in its own way.This study was supported by the Engineering and Physical Sciences Research Council (EPSRC)

Proactive Mechanisms for Video-on-Demand Content Delivery

Video delivery over the Internet is the dominant source of network load all over the world. Especially VoD streaming services such as YouTube, Netflix, and Amazon Video have propelled the proliferation of VoD in many peoples' everyday life. VoD allows watching video from a large quantity of content at any time and on a multitude of devices, including smart TVs, laptops, and smartphones. Studies show that many people under the age of 32 grew up with VoD services and have never subscribed to a traditional cable TV service. This shift in video consumption behavior is continuing with an ever-growing number of users. satisfy this large demand, VoD service providers usually rely on CDN, which make VoD streaming scalable by operating a geographically distributed network of several hundreds of thousands of servers. Thereby, they deliver content from locations close to the users, which keeps traffic local and enables a fast playback start. CDN experience heavy utilization during the day and are usually reactive to the user demand, which is not optimal as it leads to expensive over-provisioning, to cope with traffic peaks, and overreacting content eviction that decreases the CDN's performance. However, to sustain future VoD streaming projections with hundreds of millions of users, new approaches are required to increase the content delivery efficiency. To this end, this thesis identifies three key research areas that have the potential to address the future demand for VoD content. Our first contribution is the design of vFetch, a privacy-preserving prefetching mechanism for mobile devices. It focuses explicitly on OTT VoD providers such as YouTube. vFetch learns the user interest towards different content channels and uses these insights to prefetch content on a user terminal. To do so, it continually monitors the user behavior and the device's mobile connectivity pattern, to allow for resource-efficient download scheduling. Thereby, vFetch illustrates how personalized prefetching can reduce the mobile data volume and alleviate mobile networks by offloading peak-hour traffic. Our second contribution focuses on proactive in-network caching. To this end, we present the design of the ProCache mechanism that divides the available cache storage concerning separate content categories. Thus, the available storage is allocated to these divisions based on their contribution to the overall cache efficiency. We propose a general work-flow that emphasizes multiple categories of a mixed content workload in addition to a work-flow tailored for music video content, the dominant traffic source on YouTube. Thereby, ProCache shows how content-awareness can contribute to efficient in-network caching. Our third contribution targets the application of multicast for VoD scenarios. Many users request popular VoD content with only small differences in their playback start time which offers a potential for multicast. Therefore, we present the design of the VoDCast mechanism that leverages this potential to multicast parts of popular VoD content. Thereby, VoDCast illustrates how ISP can collaborate with CDN to coordinate on content that should be delivered by ISP-internal multicast

Recommender systems : dynamic adaptation and argumentation

This thesis presents the results of a multidisciplinary research project (Agorantic) on Recommender Systems. The goal of this work was to propose new features that may render recommender systems (RS) more attractive than the existing ones. We also propose a new approach to and a reflection about evaluation. In designing the system, we wanted to address the following concerns: 1. People are getting used to receive recommendations. Nevertheless, after a few bad recommendations, users will not be convinced anymore by the RS. 2. Moreover, if these suggestions come without explanations, why people should trust it? 3. The fact that item perception and user tastes and moods vary over time is well known. Still, most recommender systems fail to offer the right level of “reactivity” that users are expecting, i.e. the ability to detect and to integrate changes in needs, preferences, popularity, etc. Suggesting a movie a week after its release might be too late. In the same vein, it could take only a few ratings to make an item go from not advisable to advisable, or the other way around. 4. Users might be interested in less popular items (in the ” long tail”) and want less systematic recommendations. To answer these key issues, we have designed a new semantic and adaptive recommender system (SARS) including three innovative features, namely Argumentation, Dynamic Adaptation and a Matching Algorithm. • Dynamic Adaptation: the system is updated in a continuous way, as each new review/rating is posted. (Chapter 4) • Argumentation: each recommendation relies on and comes along with some keywords, providing the reasons that led to that recommendation. This can be seen as a first step towards a more sophisticated argumentation. We believe that, by making users more responsible for their choices, it will prevent them from losing confidence in the system. (Chapter 5) • Matching Algorithm: allows less popular items to be recommended by applying a match- ing game to users and items preferences. (Chapter 6) The system should be sensed as less intrusive thanks to relevant arguments (well-chosen words) and less responsible to unsatisfaction of the customers. We have designed a new recommender system intending to provide textually well-argued recommendations in which the end user will have more elements to make a well-informed choice. Moreover, the system parameters are dynamically and continuously updated, in order to pro- vide recommendations and arguments in phase with the very recent past. We have included a semantic level, i.e words, terms and phrases as they are naturally expressed in reviews about items. We do not use tags or pre-determined lexicon. The performances of our system are comparable to the state of the art. In addition, the fact that it provides argumentations makes it even more attractive and could enhance customers loyaltyCette thèse présente les résultats d'un projet de recherche multidisciplinaire (Agorantic) sur les systèmes de recommandation. Le but de ce travail était de proposer de nouvelles fonctionnalités qui peuvent rendre les systèmes de recommandations (RS) plus attrayants que ceux existants. Nous proposons également une nouvelle approche et une réflexion sur l'évaluation. Dans la conception du système, nous avons voulu répondre aux préoccupations suivantes: 1. Les gens s'habituent à recevoir des recommandations. Néanmoins, après quelques mauvaises recommandations, les utilisateurs ne seront plus convaincus par les RS. 2. En outre, si ces suggestions viennent sans explication, pourquoi les gens devraient les suivre ? 3. Le fait que la perception, les goûts et les humeurs des utilisateurs goûts varient au fil du temps est bien connue. Pourtant, la plupart des systèmes de recommandation ne parviennent pas à offrir le bon niveau de «réactivité» que les utilisateurs attendent, c'est à dire la capacité de détecter et d'intégrer des changements dans les besoins, les préférences, la popularité, etc. Recommander un film une semaine après sa sortie pourrait être trop tard. 4. L'utilisateur pourrait être intéressé par des articles moins populaires (dans la «longue traine»), c'est à dire des recommandations moins systématiques. Pour répondre à ces questions clés, nous avons conçu un nouveau système de recommandation sémantique et adaptatif (SRAS), comportant trois fonctionnalités innovantes, à savoir l'argumentation, l'adaptation dynamique et un algorithme d'appariement. • Adaptation dynamique: le système est mis à jour de façon continue, à chaque nouvelle note / évènement. (Chapitre 4) • Argumentation: chaque recommandation présente les raisons qui ont conduit à cette recommandation. Cela peut être considéré comme une première étape vers une argumentation plus sophistiqué. Notre volonté est de rendre les utilisateurs plus responsables de leur choix, en leur donnant le maximum d'informations. (Chapitre 5) • Algorithme d'appariement: permet aux articles les moins populaires d'être recommandés aux utilisateurs. (Chapitre 6) Nous avons conçu un nouveau système de recommandation capable de générer des recommandations textuellement bien argumentées dans lequel l'utilisateur final aura plusieurs éléments pour faire un choix éclairé. En outre, les paramètres du système sont dynamiquement et continuellement mis à jour, afin de fournir des recommandations et des arguments en la phase avec le passé très récent. Nous avons inclus un niveau sémantique, c'est à dire les mots, termes et expressions comme ils sont naturellement exprimés dans les commentaires utilisateurs. Nous n'utilisons pas d'étiquettes ou lexique pré-déterminé. Les performances de notre système sont comparables à l'état de l'art. En outre, le fait qu'il génère un argumentaire le rend encore plus attrayant et pourrait renforcer la fidélité des utilisateur