Multi-video streaming allows the viewer to interact with the stream by choosing andswitching between several different camera angles to view the stream from. For this reportwe implement and evaluate adaptive multi-video streaming with dash.js. With the help ofdash.js and self-made additions, multiple parallel video streams which are synchronized intime are implemented to provide a good user experience with smooth switching betweenstreams. These streams will be delivered according to dash.js own HTTP-based AdaptiveStreaming algorithms to achieve adaptive streams for varying conditions. In order to optimizethe usage of the available bandwidth in terms of video quality in a multi-videoenvironment we implement probabilities of camera switching to adapt qualities and allocatedbandwidth of streams. By utilizing the functions of dash.js we create two prefetchingpolicies and analyze these results together with the standard non-prefetch dash.js implementationin a multi-view video environment. Our results present the improvements interms of stalling with a prefetch implementation and the possibility of a good policy tofurther optimize a multi-view video implementation in terms of stalling, quality and bandwidthusage. Evaluation of dash.js compatibility for a multi-view video environment is alsodiscussed where pros and cons of dash.js in their current state are presented.

HTTP Adaptive Streaming (HAS) has now become ubiquitous, and it accounts for a large proportion of multimedia delivery over the Internet. Consequently, it poses new challenges for content providers and network operators. In this study, we aim to improve the user’s Quality of Experience (QoE) for HAS using from two main approaches including client centric approach and network assisted approach. In the client centric approach, we address the issue of enhancing the client’s QoE by proposing a fuzzy logic–based video bitrate adaptation and prediction mechanism for Dynamic Adaptive Streaming over HTTP (DASH) players. This adaptation mechanism allows HAS players to take appropriate actions sooner than existing methods to prevent playback interruptions caused by buffer underrun and reduce the ON-OFF traffic phenomena, which causes instability and unfairness among competing players. Our results show that compared to other studied methods, our proposed method has two advantages: better fairness among multiple competing players by almost 50% on average and as much as 80% as indicated by Jain’s fairness index, and better perceived quality of video by almost 8% on average and as much as 17%, according to the eMOS model. In the network assisted approach, we propose a novel mechanism for HAS stream adaptation in the context of wireless mobile networks. The proposed mechanism leverages recent advances in the 3GPP DASH specification, including the optional feature of QoE measurement and reporting for DASH clients. As part of the proposed mechanism, we formulate a utility-maximization problem that incorporates factors influencing QoE to specify the optimum value of Quality of Service (QoS)-related parameters for HAS streams within a wireless mobile network. The results of our simulations demonstrate that our proposed system results in better perceived quality of video, measured by Mean Opinion Score (MOS), by almost 7% on average, while lowering the freezing period by almost 20% on average across HAS users when compared to other approaches where HAS users only rely on local adaptation logics.

The subject of this term project is the analysis and description of possibilities in video streaming optimization, and the design of a client/server application to implement optimized video streaming in practice. The client side of the application is realized with a mobileapplication for the Android platform, which in addition to video streaming implements the functionality of a file manager over directories and files saved in the cloud.

This thesis is focused on the subjective measurement of the quality of experience on streaming video through a mobile application. After wide analysis of possibilities how to realize subjective measurement of quality of experience, the mobile and web application were created. These tools enable to obtain user ratings of streaming videos and then evaluate them. The user ratings are stored to the central database via secured REST API.

Le trafic vidéo s’est considérablement accru et est prévu de doubler pour représenter 82% du trafic Internet d’ici 2021. Une telle croissance surcharge les fournisseurs de services Internet (ISP), nuisant à la Qualité d’Expérience (QoE) perçue par les utilisateurs. Cette thèse vise à améliorer la QoE des utilisateurs de streaming vidéo sans hypothèse de changement d’infrastructure physique des opérateurs. Pour cela, nous combinons les technologies de caching réseau, de streaming HTTP adaptatif (HAS), et de transport multipath. Nous explorons d’abord l’interaction entre HAS et caching, pour montrer que les algorithmes d’adaptation de qualité vidéo ont besoin de savoir qu’il y a un cache et ce qui y est stocké, et proposons des algorithmes bénéficiant de cette connaissance. Concluant sur la difficulté d’obtenir la connaissance de l’état du cache, nous étudions ensuite un système de distribution vidéo à large échelle, où les caches sont représentés par un réseau de distribution du contenu (CDN). Un CDN déploie des caches à l’intérieur des réseaux des ISP, et dispose de ses propres serveurs externes. L’originalité du problème vient de l’hypothèse que nous faisons que l’utilisateur est simultanément connecté à 2 ISP. Ceci lui permet d’accéder en multipath aux serveurs externes aux ISP (pouvant ainsi accroître le débit mais chargeant plus les ISP), ou streamer le contenu depuis un cache plus proche mais avec un seul chemin. Ce désaccord entre les objectifs du CDN et de l’ISP conduit à des performances sous-optimales. Nous développons un schéma de collaboration entre ISP et CDN qui permet de nous rapprocher de l’optimal dans certains cas, et discutons l’implémentation pratique. / Video traffic volume grew considerably in recent years and is forecasted to reach 82% of the total Internet traffic by 2021, doubling its net volume as compared to today. Such growth overloads Internet Service Providers' networks (ISPs), which negatively impacts users' Quality of Experience (QoE). This thesis attempts to tackle the problem of improving users' video QoE without relying on network upgrades. For this, we have chosen to combine such technologies as in-network caching, HTTP Adaptive Streaming (HAS), and multipath data transport. We start with exploration of interaction between HAS and caching; we confirm the need of cache-awareness in quality adaptation algorithms and propose such an extension to a state-of-the-art optimisation-based algorithm. Concluding on the difficulty of achieving cache-awareness, we take a step back to study a video delivery system on a large scale, where in-network caches are represented by Content Delivery Networks (CDNs). They deploy caches inside ISPs and dispose of their own outside video servers. As a novelty, we consider users to have a simultaneous connectivity to several ISP networks. This allows video clients either to access outside multipath servers with aggregate bandwidth (which may increase their QoE, but will also bring more traffic into ISP), or stream their content from a closer cache through only single connectivity (bringing less traffic into ISP). This disagreement in ISP and CDN objectives leads to suboptimal system performance. In response to this, we develop a collaboration scheme between two actors, performance of which can approach optimal boundary for certain settings, and discuss its practical implementation.

Avec l'hétérogénéité des réseaux et la demande croissante de services multimédias, la Qualité d'Expérience (QoE) devient un facteur déterminant de réussite ou d’échec de ces services. Dans cette thèse, nous avons d’abord analysé l'impact des paramètres de qualité sur l'engagement de l'utilisateur, afin de comprendre l’effet des paramètres vidéo (temps de démarrage, qualité intrinsèque, taux de blocage) et la popularité du service sur l'engagement de l'utilisateur. Nos résultats ont montré que le taux de blocage et la popularité du service sont des paramètres critiques qui impactent fortement la satisfaction et l'engagement de l’utilisateur, tandis que, le temps de démarrage a moins d’impact. Ensuite, les approches subjectives de QoE comme le Mean Opinion Score (MOS) ont été traité dans cette thèse, où les utilisateurs sont appelés à donner une note de satisfaction en fonction des informations de contextes. Une analyse statistique des résultats obtenus à partir de tests utilisateurs a montré que le type de terminal et de contenu sont des paramètres qui impactent fortement le MOS. En plus, des modèles mathématiques d’estimation de QoE en fonction des informations de contextes ont été proposés. Enfin, deux techniques d’optimisations ont été proposées : Le MDASH (MOS Dynamic Adaptive Streaming over HTTP) pour l’optimisation vidéo de flux partageant les mêmes ressources, de manière à garantir une certaine QoE pour tous les utilisateurs. Et une optimisation de livraison, basée sur des fonctions d’utilités dans laquelle, une fonction globale d'utilité est calculée en fonction de différentes contraintes (qui dépendent fortement des stratégies des acteurs de la chaîne de livraison) / With the network heterogeneity and increasing demand of multimedia services, Quality of Experience (QoE) becomes a crucial determinant of the success or failure of these services. In this thesis, we first propose to analyze the impact of quality metrics on user engagement, in order to understand the effects of video metrics (video startup time, average bitrate, buffering ratio) and content popularity on user engagement. Our results show that video buffering and content popularity are critical parameters which strongly impacts the end-user’s satisfaction and user engagement, while the video startup time appears as less significant. On other hand, we consider subjective approaches such as the Mean Opinion Score (MOS) for evaluating QoE, in which users are required to give their assessment according to contextual information. A detailed statistical analysis of our study shows the existence of non-trivial parameters impacting MOS (the type of device and the content type). We propose mathematical models to develop functional relationships between the QoE and the context information which in turn permits us to estimate the QoE. A video content optimization technique called MDASH (for MOS Dynamic Adaptive Streaming over HTTP) is proposed, which improves the perceived QoE for different video sessions sharing the same local network, while taking QoE fairness among users as a leitmotiv. We also propose a utility-based approach for video delivery optimization, in which a global utility function is computed based on different constraints (e.g. target strategies coming from the actors of the delivery chain)

Les vidéos omnidirectionnelles, également appelées vidéos sphériques ou vidéos360°, sont des vidéos avec des pixels enregistrés dans toutes les directions de l’espace. Un utilisateur qui regarde un tel contenu avec un Casques de Réalité Virtuelle (CRV) peut sélectionner la partie de la vidéo à afficher, usuellement nommée viewport, en bougeant la tête. Pour se sentir totalement immergé à l’intérieur du contenu, l’utilisateur a besoin de voir au moins 90 viewports par seconde en 4K. Avec les technologies de streaming traditionnelles, fournir une telle qualité nécessiterait un débit de plus de100 Mbit s−1, ce qui est bien trop élevé. Dans cette thèse, je présente mes contributions pour rendre possible le streaming de vidéos omnidirectionnelles hautement immersives sur l’Internet. On peut distinguer six contributions : une proposition d’architecture de streaming viewport adaptatif réutilisant une partie des technologies existantes ; une extension de cette architecture pour des vidéos à six degrés de liberté ; deux études théoriques des vidéos à qualité spatiale non-homogène; un logiciel open source de manipulation des vidéos 360°; et un jeu d’enregistrements de déplacements d’utilisateurs regardant des vidéos 360°. / Omnidirectional videos, also denoted as spherical videos or 360° videos, are videos with pixels recorded from a given viewpoint in every direction of space. A user watching such an omnidirectional content with a Head Mounted Display (HMD) can select the portion of the videoto display, usually denoted as viewport, by moving her head. To feel high immersion inside the content a user needs to see viewport with 4K resolutionand 90 Hz frame rate. With traditional streaming technologies, providing such quality would require a data rate of more than 100 Mbit s−1, which is far too high compared to the median Internet access band width. In this dissertation, I present my contributions to enable the streaming of highly immersive omnidirectional videos on the Internet. We can distinguish six contributions : a viewport-adaptive streaming architecture proposal reusing a part of existing technologies ; an extension of this architecture for videos with six degrees of freedom ; two theoretical studies of videos with non homogeneous spatial quality ; an open-source software for handling 360° videos ; and a dataset of recorded users’ trajectories while watching 360° videos.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-04-06T13:08:48Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) A CONTROL-THEORETIC APPROACH FOR ADAPTIVE STREAMING OVER HTT.pdf: 5118913 bytes, checksum: fc08706cd245c8c0de45e4db894cb0a5 (MD5) / Made available in DSpace on 2016-04-06T13:08:48Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) A CONTROL-THEORETIC APPROACH FOR ADAPTIVE STREAMING OVER HTT.pdf: 5118913 bytes, checksum: fc08706cd245c8c0de45e4db894cb0a5 (MD5) Previous issue date: 2015-08-24 / FACEPE / Video applications account for a large percentage of Internet traffic, and their utilization tends to grow. In this scenario, multimedia providers are required to deliver video and/or audio content in a seamless manner, regardless of network conditions and client device capabilities. However, current commercial services are not efficient enough, and the academic proposals do not take full advantage of their potential. To this end, we propose a control-theoretic adaptive streaming over HTTP system, which adapts the video quality to the network conditions in a video session. With this system, we aim at delivering video with the highest Quality of Experience possible, while leveraging the available network resources. Our system is located at the client side, and comprises a control system and a state machine. Together, they select the video level that is requested to the server, a standard HTTP server, which sends video chunks as the client requests them. To select the control system and state machine that best fit our requirements, we perform several experiments with different parameter values combinations. Our parameter selection is accomplished in two steps, the last one resulting in the combination that best fits our needs. Then, we compare our system performance to an existent system in the academia. Our experimental results show the benefits of providing more aggressive decisions on the video quality selection. However, they also show that such aggressiveness may, sometimes, compromise the system performance. Overall, though, our system outperforms the baseline of our experiments. / A computação em nuvem é um paradigma computacional que vem sendo amplamente utilizado ao longo dos últimos anos devido as suas características de provisionamento de recursos de forma escalável, onde o usuário paga apenas por aquilo que consumir. Esse modelo computacional possibilita que diversos serviços sejam ofertados a partir da sua Infraestrutura como Serviço (IaaS - Infrastructure as a Service). Porém, a falha de componentes dos recursos da nuvem é algo bastante comum e que afeta diretamente a disponibilidade dos serviços que os utilizam. Garantir alta disponibilidade em serviços na nuvem é um desafio para provedores, que podem utilizar de técnicas como análise de sensibilidade para identificar gargalos de disponibilidade ou ainda fazer uso de mecanismos de tolerância à falhas para atingir melhores resultados de confiabilidade. Desse modo, esse trabalho tem como proposta realizar a avaliação de disponibilidade em arquiteturas para o provimento do serviço de VoD streaming na nuvem, baseado na plataforma Eucalyptus. O trabalho está dividido em dois estudos de caso. Primeiro, apresentamos uma arquitetura básica, sem mecanismos de redundância. Através de modelagem hierárquica formada por diagramas de blocos de confiabilidade (RBD - Reliability Block Diagram) e cadeias de Markov de tempo contínuo (CTMC - Continuous Time Markov Chain), realizamos a avaliação da disponibilidade para essa arquitetura, e em seguida alcançamos a validação do modelo que representa o funcionamento do serviço. Por fim, aplicamos a técnica de análise de sensibilidade paramétrica para identificar gargalos de disponibilidade na arquitetura básica. As análises seguintes que foram esse estudo de caso observam a disponibilidade do serviço em arquiteturas com redundância no modo warm-stand guiadas por índices de sensibilidade, apresentando ao final uma comparação dos valores de disponibilidade para cada arquitetura obtida a partir de componentes redundantes. O segundo estudo de caso analisa o comportamento da disponibilidade total do serviço a partir da variação no tempo de ativação dos mecanismos redundantes. Nossos resultados expressaram que a adoção de componentes redundantes no modo warm-stand na infraestrutura da nuvem proporciona ganhos de disponibilidade no serviço analisado. Também observamos que a variação do tempo de ativação do componente de maior confiabilidade tem maior impacto na disponibilidade total do serviço de VoD streaming.

Předložená dizertační práce se zabývá moderními algoritmy pro kódovaní videosekvencí, zejména algoritmem High Efficiency Video Coding, a jeho použítím v prostředí online streamování. Vzhledem k tomu, že chování koncových diváků směřuje ke sledování video obsahu kdykoli a kdekoli, způsob, jakým je obsah doručen k divákovi, se stává stejně důležitým, jakým je samotné kódování. V této práci se zaměřujeme na užití HEVC ve službách založených na HTTP adaptivním streamování, zejména ve službách využívajích DASH. Dále se zabýváme dalšími aspekty, které mají vliv na kvalitu zážitku (Quality of Experience) tak, jak jej vnímá koncový uživatel. Takovými jsou na příklad přítomnost reklamy či další systémové parametry. Abychom mohli sbírat názory uživatelů, pro naše experimenty často používáme crowdsourcing. Z tohoto důvodu je část této práce věnována samotnému crowdsourcingu a tomu, jak jej lze využít pro hodnocení kvality videa.

The proliferation of new devices (such as smartphones and tablets) promotes new multimedia services (e.g. user-generated live video broadcasting), as well as new streaming techniques (e.g. rate-adaptive streaming). As a matter of fact, scientists observe a formidable, sustainable growth of Internet traffic related to video streaming. Yet, network infrastructures struggle to cope with this growth and it is now frequent that a delivery network is insufficiently provisioned. Such underprovisioning problem is more severe for live videos due to its real-time requirement. In this thesis, we focus on bandwidth efficient video delivery solutions for live streaming in underprovisioned video delivery networks. Specifically, we have two main contributions: (1) a user-generated live videos sharing system based on peer-to-peer (P2P) technique, and (2) a live rate-adaptive streaming system based on Content Delivery Network (CDN). First of all, we built an multioverlay P2P video sharing system which allows Internet users to broadcast their own live videos. Typically, such a system consists of multiple P2P live video streaming systems, and faces the problem of finding a suitable allocation of peer upload bandwidth. We designed various bandwidth allocation algorithms for this problem and showed how optimal solutions can be efficiently computed. Then, we studied the problem of delivering live rate-adaptive streams in the CDN. We identified a discretize streaming model for multiple live videos in modern CDNs. We formulated a general optimization problem through Integer Linear Programming (ILP) and showed that it is NP-complete. Further, we presented a fast, easy to implement, and near-optimal algorithm with approved approximation ratios for a specific scenario. This work is the first step towards streaming multiple live rate-adaptive videos in CDN and provides a fundamental theoretical basis for deeper investigation. Last, we further extended the discretized streaming model into an user-centric one which maximizes the overall satisfaction of an user population. Further, we presented a practical system, which efficiently utilizes CDN infrastructure to deliver live video streams to viewers in dynamic and large-scale CDNs. The benefits of our approaches on reducing the CDN infrastructure capacity is validated through a set of realistic trace-driven large-scale simulations. All in one, this thesis explores bandwidth efficient live video delivery solutions in underprovisioned delivery network for multiple streaming technologies. The aim is to maximally utilize the bandwidth of relay nodes (peers in P2P and forwarding equipments in CDN) to achieve an optimization goal.