Building Internet caching systems for streaming media delivery

The proxy has been widely and successfully used to cache the static Web objects fetched by a client so that the subsequent clients requesting the same Web objects can be served directly from the proxy instead of other sources faraway, thus reducing the server\u27s load, the network traffic and the client response time. However, with the dramatic increase of streaming media objects emerging on the Internet, the existing proxy cannot efficiently deliver them due to their large sizes and client real time requirements.;In this dissertation, we design, implement, and evaluate cost-effective and high performance proxy-based Internet caching systems for streaming media delivery. Addressing the conflicting performance objectives for streaming media delivery, we first propose an efficient segment-based streaming media proxy system model. This model has guided us to design a practical streaming proxy, called Hyper-Proxy, aiming at delivering the streaming media data to clients with minimum playback jitter and a small startup latency, while achieving high caching performance. Second, we have implemented Hyper-Proxy by leveraging the existing Internet infrastructure. Hyper-Proxy enables the streaming service on the common Web servers. The evaluation of Hyper-Proxy on the global Internet environment and the local network environment shows it can provide satisfying streaming performance to clients while maintaining a good cache performance. Finally, to further improve the streaming delivery efficiency, we propose a group of the Shared Running Buffers (SRB) based proxy caching techniques to effectively utilize proxy\u27s memory. SRB algorithms can significantly reduce the media server/proxy\u27s load and network traffic and relieve the bottlenecks of the disk bandwidth and the network bandwidth.;The contributions of this dissertation are threefold: (1) we have studied several critical performance trade-offs and provided insights into Internet media content caching and delivery. Our understanding further leads us to establish an effective streaming system optimization model; (2) we have designed and evaluated several efficient algorithms to support Internet streaming content delivery, including segment caching, segment prefetching, and memory locality exploitation for streaming; (3) having addressed several system challenges, we have successfully implemented a real streaming proxy system and deployed it in a large industrial enterprise

Advanced Free Viewpoint Video Streaming Techniques

Free-viewpoint video is a new type of interactive multimedia service allowing users to control their viewpoint and generate new views of a dynamic scene from any perspective. The uniquely generated and displayed views are composed from two or more high bitrate camera streams that must be delivered to the users depending on their continuously changing perspective. Due to significant network and computational resource requirements, we proposed scalable viewpoint generation and delivery schemes based on multicast forwarding and distributed approach. Our aim was to find the optimal deployment locations of the distributed viewpoint synthesis processes in the network topology by allowing network nodes to act as proxy servers with caching and viewpoint synthesis functionalities. Moreover, a predictive multicast group management scheme was introduced in order to provide all camera views that may be requested in the near future and prevent the viewpoint synthesizer algorithm from remaining without camera streams. The obtained results showed that even 42% traffic decrease can be realized using distributed viewpoint synthesis and the probability of viewpoint synthesis starvation can be also significantly reduced in future free viewpoint video services

Enhancing Video Streaming Quality of DASH over Cloud/Edge Integrated Networks

With the advancement of mobile technologies and the popularity of mobile devices, mobile video streaming applications/services have increased considerably in recent years. Dynamic Adaptive Streaming over HTTP (DASH) or MPEG-DASH is one of the most widely used video streaming techniques over the Internet. It adapts video sending bit rate according to available network resources, however, in case of low bandwidth, DASH performs poorly, which will cause video quality degradation and video stalling. Mobile Edge Computing (MEC) or Multi-access Edge Computing, in connection with the backend cloud has been used to reduce latency and overcome some of the video quality degradation problems for mobile video streaming services. However, an end user might be suffering from video quality drop downs when s/he moves out from the coverage of one node to another or when the mobile network condition goes down. To tackle the degradation problems and assure enhanced video streaming quality, a novel follow-me Edge Node Prefetching (ENP) scheme was proposed and developed in the project, by prefetching video segments in advance in the upcoming node used by the end-user. A test bed was set up consisting of a backend cloud (OpenStack), two edge nodes (LXD Containers) and one mobile device, the ENP algorithm was implemented on the cloud server and client sides. Experiments were carried out for the DASH streaming service based on Dash.js from the DASH Industry Forum. Preliminary results show that the ENP scheme can maintain higher video quality and less service migration time when moving from one mobile node to another, when compared to existing approaches, i.e. live migration in Follow-me-Edge and the C-up schemes. The proposed scheme could be useful in smart city applications or providing seamless mobile video streaming services in Cloud/Edge integrated networks.Ibrahim Mohammedamee

QoE over-the-top multimédia em redes sem fios

One of the goals of an operator is to improve the Quality of Experience (QoE) of a client in networks where Over-the-top (OTT) content is being delivered. The appearance of services like YouTube, Netflix or Twitch, where in the first case it contains more than 300 hours of video per minute in the platform, brings issues to the managed data networks that already exist, as well as challenges to fix them. Video traffic corresponds to 75% of the whole transmitted data on the Internet. This way, not only the Internet did become the ’de facto’ video transmission path, but also the general data traffic continues to exponentially increase, due to the desire to consume more content. This thesis presents two model proposals and architecture that aim to improve the users’ quality of experience, by predicting the amount of video in advance liable of being prefetched, as a way to optimize the delivery efficiency where the quality of service cannot be guaranteed. The prefetch is done in the clients’ closest cache server. For that, an Analytic Hierarchy Process (AHP) is used, where through a subjective method of attribute comparison, and from the application of a weighted function on the measured quality of service metrics, the amount of prefetch is achieved. Besides this method, artificial intelligence techniques are also taken into account. With neural networks, there is an attempt of selflearning with the behavior of OTT networks with more than 14.000 hours of video consumption under different quality conditions, to try to estimate the experience felt and maximize it, without the normal service delivery degradation. At last, both methods are evaluated and a proof of concept is made with users in a high speed train.Um dos objetivos de um operador é melhorar a qualidade de experiência do cliente em redes onde existem conteúdos Over-the-top (OTT) a serem entregues. O aparecimento de serviços como o YouTube, Netflix ou Twitch, onde no primeiro caso são carregadas mais de 300 horas de vídeo por minuto na plataforma, vem trazer problemas às redes de dados geridas que já existiam, assim como desafios para os resolver. O tráfego de vídeo corresponde a 75% de todos os dados transmitidos na Internet. Assim, não só a Internet se tornou o meio de transmissão de vídeo ’de facto’, como o tráfego de dados em geral continua a crescer exponencialmente, proveniente do desejo de consumir mais conteúdos. Esta tese apresenta duas propostas de modelos e arquitetura que pretendem melhorar a qualidade de experiência do utilizador, ao prever a quantidade de vídeo em avanço passível de ser précarregado, de forma a optimizar a eficiência de entrega das redes onde a qualidade de serviço não é possível de ser garantida. O pré-carregamento dos conteúdos é feito no servidor de cache mais próximo do cliente. Para tal, é utilizado um processo analítico hierárquico (AHP), onde através de um método subjetivo de comparação de atributos, e da aplicação de uma função de valores ponderados nas medições das métricas de qualidade de serviço, é obtida a quantidade a pré-carregar. Além deste método, é também proposta uma abordagem com técnicas de inteligência artificial. Através de redes neurais, há uma tentativa de auto-aprendizagem do comportamento das redes OTT com mais de 14.000 horas de consumo de vídeo sobre diferentes condições de qualidade, para se tentar estimar a experiência sentida e maximizar a mesma, sem degradação da entrega de serviço normal. No final, ambos os métodos propostos são avaliados num cenário de utilizadores num comboio a alta velocidade.Mestrado em Engenharia de Computadores e Telemátic

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

Proxy Support for HTTP Adaptive Streaming

Not long ago streaming video over the Internet included only short clips of low quality video. Now the possibilities seem endless as professional productions are made available in high definition. This explosion of growth is the result of several factors, such as increasing network performance, advancements in video encoding technology, improvements to video streaming techniques, and a growing number of devices capable of handling video. However, despite the improvements to Internet video streaming this paradigm is still evolving. HTTP adaptive streaming involves encoding a video at multiple quality levels then dividing those quality levels into small chunks. The player can then determine which quality level to retrieve the next chunk from in order to optimize video playback when considering the underlying network conditions. This thesis first presents an experimental framework that allows for adaptive streaming players to be analyzed and evaluated. Evaluation is beneficial because there are several concerns with the adaptive video streaming ecosystem such as achieving a high video playback quality while also ensuring stable playback quality. The primary contribution of this thesis is the evaluation of prefetching by a proxy server as a means to improve streaming performance. This work considers an implementation of a proxy server that is functional with the extremely popular Netflix streaming service, and it is evaluated using two Netflix players. The results show its potential to improve video streaming performance in several scenarios. It effectively increases the buffer capacity of the player as chunks can be prefetched in advance of the player's request then stored on the proxy to be quickly delivered once requested. This allows for degradation in network conditions to be hidden from the player while the proxy serves prefetched data, preventing a reduction to the video quality as a result of an overreaction by the player. Further, the proxy can reduce the impact of the bottleneck in the network, achieving higher throughput by utilizing parallel connections to the server

Caching and prefetching for efficient video services in mobile networks

Cellular networks have witnessed phenomenal traffic growth recently fueled by new high speed broadband cellular access technologies. This growth is in large part driven by the emergence of the HTTP Adaptive Streaming (HAS) as a new video delivery method. In HAS, several qualities of the same videos are made available in the network so that clients can choose the quality that best fits their bandwidth capacity. This strongly impacts the viewing pattern of the clients, their switching behavior between video qualities, and thus beyond on content delivery systems.Our first contribution consists in providing an analysis of a real HAS dataset collected in France and provided by the largest French mobile operator. Firstly, we analyze and model the viewing patterns of VoD and live streaming HAS sessions and we propose a new cache replacement strategy, named WA-LRU. WA-LRU leverages the time locality of video segments within the HAS content. We show that WA-LRU improves the cache hit-ratio mostly at the loading phase while it reduces significantly the processing overhead at the cache.In our second contribution, we analyze and model the adaptation logic between the video qualities based on empirical observations. We show that high switching behaviors lead to sub optimal caching performance, since several versions of the same content compete to be cached. In this context we investigate the benefits of a Cache Friendly HAS system (CF-DASH) which aims at improving the caching efficiency in mobile networks and to sustain the quality of experience of mobile clients. We evaluate CF-dash based on trace-driven simulations and test-bed experiments. Our validation results are promising. Simulations on real HAS traffic show that we achieve a significant gain in hit-ratio that ranges from 15% up to 50%.In the second part of this thesis, we investigate the mobile video prefetching opportunities. Online media services are reshaping the way video content is watched. People with similar interests tend to request same content. This provides enormous potential to predict which content users are interested in. Besides, mobile devices are commonly used to watch videos which popularity is largely driven by their social success. We design a system, named "Central Predictor System (CPsys)", which aims at predicting and prefetching relevant content for each mobile client. To fine tune our prefetching system, we rely on a large dataset collected from a large mobile carrier in Europe. The rationale of our prefetching strategy is first to form a graph and build implicit or explicit ties between similar users. On top of this graph, we propose the Most Popular and Most Recent (MPMR) policy to predict relevant videos for each user. We show that CPSys can achieve high performance as regards prediction correctness and network utilization efficiency. We further show that CPSys outperforms other prefetching schemes from the state of the art. At the end, we provide a proof-of-concept implementation of our prefetching system.Les réseaux cellulaires ont connu une croissance phénoménale du trafic alimentée par les nouvelles technologies d’accès cellulaire à large bande. Cette croissance est tirée en grande partie par le trafic HTTP adaptatif streaming (HAS) comme une nouvelle technique de diffu- sion de contenus audiovisuel. Le principe du HAS est de rendre disponible plusieurs qualités de la même vidéo en ligne et que les clients choisissent la meilleure qualité qui correspond à leur bande passante. Chaque niveau d’encodage est segmenté en des petits vidéos qu’on appelle segments ou chunks et dont la durée varie entre 2 à 10 secondes. L’émergence du HAS a introduit des nouvelles contraintes sur les systèmes de livraison des contenus vidéo en particulier sur les systèmes de cache. Dans cette thèse, nous nous intéressons à l’étude de cet impact et à proposer des algorithmes et des solutions qui optimisent les fonctionnalités de ces systèmes. D’autre part, la consommation des contenus est fortement impactée par les nouvelles technologies du Web2.0 tel que l’émergence des réseaux sociaux. Dans cette thèse, nous exploitons les réseaux sociaux afin de proposer un service de préchargement des contenus VoD sur terminaux mobiles. Notre solution permet l’amélioration de la QoE des utilisateurs et permet de bien gérer les ressources réseaux mobile.Nous listons nos contributions comme suit :Notre première contribution consiste à mener une analyse détaillée des données sur un trafic HAS réel collecté en France et fournie par le plus grand opérateur de téléphonie mobile du pays. Tout d’abord, nous analysons et modélisons le comportement des clients qui demandent des contenus catch-up et live. Nous constatons que le nombre de requêtes par segment suit deux types de distribution : La loi log-normal pour modéliser les 40 premiers chunks par session de streaming, ensuite on observe une queue qui peut être modélisé par la loi de Pareto. Cette observation suggère que les clients ne consomment pas la totalité du contenu catch-up. On montre par simulation que si le cache implémente des logiques de caching qui ne tiennent pas en compte les caractéristiques des flux HAS, sa performance diminuerait considérablement.Dans ce contexte, nous proposons un nouvel algorithme de remplacement des contenus que nous appelons Workload Aware-LRU (WA-LRU). WA-LRU permet d’améliorer la performance des systèmes de cache en augmentant le Hit-Ratio en particulier pour les premiers segments et en diminuant le temps requis pour la mise à jour de la liste des objets cachés. En fonction de la capacité du cache et de la charge du trafic dans le réseau, WA-LRU estime un seuil sur le rang du segment à cacher. Si le rang du chunk demandé dépasse ce seuil, le chunk ne sera pas caché sinon il sera caché. Comme WA-LRU dépend de la charge du trafic dans le réseau, cela suppose que le seuil choisit par WA-LRU est dynamique sur la journée. WA-LRU est plus agressif pendant les heures chargées (i.e. il cache moins de chunks, ceux qui sont les plus demandés) que pendant les heures creuses où le réseau est moins chargé.Dans notre deuxième contribution, nous étudions plus en détail les facteurs qui poussent les clients HAS à changer de qualité lors d’une session vidéo. Nous modélisons également ce changement de qualité en se basant sur des données empiriques provenant de notre trace de trafic. Au niveau du cache, nous montrons que le changement fréquent de qualité crée une compétition entre les différents profiles d’encodages. Cela réduit les performances du système de cache. Dans ce contexte, nous proposons Cache Friendly-DASH (CF-DASH), une implémentation d’un player HAS compatible avec le standard DASH, qui assure une meilleure stabilité du player. Nous montrons à travers des simulations et des expérimentations que CF- DASH améliore expérience client et permet aussi d’atteindre un gain significatif du hit-ratio qui peut varier entre 15% à 50%.Dans la deuxième partie de cette thèse, nous proposons un système de préchargement de contenus vidéos sur terminaux mobile. La consommation des contenus vidéo en ligne est fortement impactée par les nouvelles technologies du Web2.0 et les réseaux sociaux. Les personnes qui partagent des intérêts similaires ont tendance à demander le même contenu. Cela permet de prédire le comportement des clients et identifier les contenus qui peuvent les intéresser. Par ailleurs, les smartphones et tablettes sont de plus en plus adaptés pour visionner des vidéos et assurer une meilleure qualité d’expérience. Dans cette thèse, nous concevons un système qu’on appelle CPSys (Central Predictor System) permettant d’identifier les vidéos les plus pertinentes pour chaque utilisateur. Pour bien paramétrer notre système de préchargement, nous analysons des traces de trafic de type User Generated Videos (UGC). En particulier, nous analysons la popularité des contenus YouTube et Facebook, ainsi que l’évolution de la popularité des contenus en fonction du temps. Nous observons que 10% des requêtes se font sur une fenêtre de temps d’une heure après avoir mis les vidéos en ligne et 40% des requêtes se font sur une fenêtre de temps de un jour. On présente aussi des analyses sur le comportement des clients. On observe que la consommation des contenus vidéo varie significativement entre les clients mobiles. On distingue 2 types de clients :• les grands consommateurs : Ils forment une minorité mais consomment plusieurs vidéos sur une journée.• lespetitsconsommateurs:Ilsformentlamajoritédesclientsmaisconsommentquelques vidéos par jour voir sur une période plus longue.On s’appuyant sur ces observations, notre système de préchargement adapte le mode de pré- chargement selon le profil utilisateur qui est déduit à partir de l’historique de la consommation de chaque client.Dans un premier temps, CPSys crée un graphe regroupant les utilisateurs qui sont similaires. Le graphe peut être soit explicite (type Facebook) ou implicite qui est construit à la base des techniques de colllaborative filtering dérivés des systèmes de recommandations. Une fois le graphe est créé, nous proposons la politique Most Popular Most Recent (MPMR) qui permet d’inférer quel contenu doit-on précharger pour chaque utilisateur. MPMR trie les vidéos candidats selon la popularité locale du contenu définit comme le nombre de vues effectués par les voisins les plus similaires, ensuite MPMR donne la priorité aux contenus les plus frais. Nous montrons que CPSys peut atteindre des performances élevées par rapport à d’autres techniques présentées dans l’état de l’art. CPSys améliore la qualité de la prédiction et réduit d’une manière significative le trafic réseau.Finalement, nous développons une preuve de concept de notre système de préchargement

Time-Shifted Prefetching and Edge-Caching of Video Content: Insights, Algorithms, and Solutions

Video traffic accounts for 82% of global Internet traffic and is growing at an unprecedented rate. As a result of this rapid growth and popularity of video content, the network is heavily burdened. To cope with this, service providers have to spend several millions of dollars for infrastructure upgrades; these upgrades are typically triggered when there is a reasonably sustained peak usage that exceeds 80% of capacity. In this context, with network traffic load being significantly higher during peak periods (up to 5 times as much), we explore the problem of prefetching video content during off-peak periods of the network even when such periods are substantially separated from the actual usage-time. To this end, we collected YouTube and Netflix usage from over 1500 users spanning at least a one-year period consisting of approximately 8.5 million videos collectively watched. We use the datasets to analyze and present key insights about user-level usage behavior, and show that our analysis can be used by researchers to tackle a myriad of problems in the general domains of networking and communication. Thereafter, equipped with the datasets and our derived insights, we develop a set of data-driven prediction and prefetching solutions, using machine-learning and deep-learning techniques (specifically supervised classifiers and LSTM networks), which anticipates the video content the user will consume based on their prior watching behavior, and prefetches it during off-peak periods. We find that our developed solutions can reduce nearly 35% of peak-time YouTube traffic and 70% of peak-time Netflix series traffic. We developed and evaluated a proof-of-concept system for prefetching video traffic. We also show how to integrate the two systems for prefetching YouTube and Netflix content. Furthermore, based on our findings from our developed algorithms, we develop a framework for prefetching video content regardless of the type of video and platform upon which it is hosted.Ph.D