Survey of Transportation of Adaptive Multimedia Streaming service in Internet

[DE] World Wide Web is the greatest boon towards the technological advancement of modern era. Using the benefits of Internet globally, anywhere and anytime, users can avail the benefits of accessing live and on demand video services. The streaming media systems such as YouTube, Netflix, and Apple Music are reining the multimedia world with frequent popularity among users. A key concern of quality perceived for video streaming applications over Internet is the Quality of Experience (QoE) that users go through. Due to changing network conditions, bit rate and initial delay and the multimedia file freezes or provide poor video quality to the end users, researchers across industry and academia are explored HTTP Adaptive Streaming (HAS), which split the video content into multiple segments and offer the clients at varying qualities. The video player at the client side plays a vital role in buffer management and choosing the appropriate bit rate for each such segment of video to be transmitted. A higher bit rate transmitted video pauses in between whereas, a lower bit rate video lacks in quality, requiring a tradeoff between them. The need of the hour was to adaptively varying the bit rate and video quality to match the transmission media conditions. Further, The main aim of this paper is to give an overview on the state of the art HAS techniques across multimedia and networking domains. A detailed survey was conducted to analyze challenges and solutions in adaptive streaming algorithms, QoE, network protocols, buffering and etc. It also focuses on various challenges on QoE influence factors in a fluctuating network condition, which are often ignored in present HAS methodologies. Furthermore, this survey will enable network and multimedia researchers a fair amount of understanding about the latest happenings of adaptive streaming and the necessary improvements that can be incorporated in future developments.Abdullah, MTA.; Lloret, J.; Canovas Solbes, A.; García-García, L. (2017). Survey of Transportation of Adaptive Multimedia Streaming service in Internet. Network Protocols and Algorithms. 9(1-2):85-125. doi:10.5296/npa.v9i1-2.12412S8512591-

Generalized Rate-Distortion Functions of Videos

Customers are consuming enormous digital videos every day via various kinds of video services through terrestrial, cable, and satellite communication systems or over-the-top Internet connections. To offer the best possible services using the limited capacity of video distribution systems, these video services desire precise understanding of the relationship between the perceptual quality of a video and its media attributes, for which we term it the GRD function. In this thesis, we focus on accurately estimating the generalized rate-distortion (GRD) function with a minimal number of measurement queries. We first explore the GRD behavior of compressed digital videos in a two-dimensional space of bitrate and resolution. Our analysis on real-world GRD data reveals that all GRD functions share similar regularities, but meanwhile exhibit considerable variations across different combinations of content and encoder types. Based on the analysis, we define the theoretical space of the GRD function, which not only constructs the groundwork of the form a GRD model should take, but also determines the constraints these functions must satisfy. We propose two computational GRD models. In the first model, we assume that the quality scores are precise, and develop a robust axial-monotonic Clough-Tocher (RAMCT) interpolation method to approximate the GRD function from a moderate number of measurements. In the second model, we show that the GRD function space is a convex set residing in a Hilbert space, and that a GRD function can be estimated by solving a projection problem onto the convex set. By analyzing GRD functions that arise in practice, we approximate the infinite-dimensional theoretical space by a low-dimensional one, based on which an empirical GRD model of few parameters is proposed. To further reduce the number of queries, we present a novel sampling scheme based on a probabilistic model and an information measure. The proposed sampling method generates a sequence of queries by minimizing the overall informativeness of the remaining samples. To evaluate the performance of the GRD estimation methods, we collect a large-scale database consisting of more than $4,000$ real-world GRD functions, namely the Waterloo generalized rate-distortion (Waterloo GRD) database. Extensive comparison experiments are carried out on the database. Superiority of the two proposed GRD models over state-of-the-art approaches are attested both quantitatively and visually. Meanwhile, it is also validated that the proposed sampling algorithm consistently reduces the number of queries needed by various GRD estimation algorithms. Finally, we show the broad application scope of the proposed GRD models by exemplifying three applications: rate-distortion curve prediction, per-title encoding profile generation, and video encoder comparison

Smart Sensor Technologies for IoT

The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT

Otimização de distribuição de conteúdos multimédia utilizando software-defined networking

The general use of Internet access and user equipments, such as smartphones, tablets and personal computers, is creating a new wave of video content consumption. In the past two decades, the Television broadcasting industry went through several evolutions and changes, evolving from analog to digital distribution, standard definition to high definition TV-channels, form the IPTV method of distribution to the latest set of technologies in content distribution, OTT. The IPTV technology introduced features that changed the passive role of the client to an active one, revolutionizing the way users consume TV content. Thus, the clients’ habits started to shape the services offered, leading to an anywhere and anytime offer of video content. OTT video delivery is a reflection of those habits, meeting the users’ desire, introducing several benefits discussed in this work over the previous technologies. However, the OTT type of delivery poses several challenges in terms of scalability and threatens the Telecommunications Operators business model, because OTT companies use the Telcos infrastructure for free. Consequently, Telecommunications Operators must prepare their infrastructure for future demand while offering new services to stay competitive. This dissertation aims to contribute with insights on what infrastructure changes a Telecommunications Operator must perform with a proposed bandwidth forecasting model. The results obtained from the forecast model paved the way to the proposed video content delivery method, which aims to improve users’ perceived Quality-of-Experience while optimizing load balancing decisions. The overall results show an improvement of users’ experience using the proposed method.A generalização do acesso à Internet e equipamentos pessoais como smartphones, tablets e computadores pessoais, está a criar uma nova onda de consumo de conteúdos multimedia. Nas ultimas duas décadas, a indústria de transmissão de Televisão atravessou várias evoluções e alterações, evoluindo da distribuição analógica para a digital, de canais de Televisão de definição padrão para alta definição, do método de distribuição IPTV, até ao último conjunto de tecnologias na distribuição de conteúdos, OTT. A tecnologia IPTV introduziu novas funcionalidades que mudaram o papel passivo do cliente para um papel activo, revolucionando a forma como os utilizadores consumem conteúdos televisivos. Assim, os hábitos dos clientes começaram a moldar os serviços oferecidos, levando à oferta de consumo de conteúdos em qualquer lugar e em qualquer altura. A entrega de vídeo OTT é um reflexo destes hábitos, indo ao encontro dos desejos dos utilizadores, que introduz inúmeras vantagens sobre outras tecnologias discutidas neste trabalho. No entanto, a entrega de conteúdos OTT cria diversos problemas de escalabilidade e ameaça o modelo de negócio das Operadoras de Telecomunicações, porque os fornecedores de serviço OTT usam a infraestrutura das mesmas sem quaisquer custos. Consequentemente, os Operadores de Telecomunicações devem preparar a sua infraestrutura para o consumo futuro ao mesmo tempo que oferecem novos serviços para se manterem competitivos. Esta dissertação visa contribuir com conhecimento sobre quais alterações uma Operadora de Telecomunicações deve executar com o modelo de previsão de largura de banda proposto. Os resultados obtidos abriram caminho para o método de entrega de conteúdos multimedia proposto, que visa ao melhoramento da qualidade de experiência do utilizador ao mesmo tempo que se optimiza o processo de balanceamento de carga. No geral os testes confirmam uma melhoria na qualidade de experiência do utilizador usando o método proposto.Mestrado em Engenharia de Computadores e Telemátic

Aproximaciones en la preparación de contenido de vídeo para la transmisión de vídeo bajo demanda (VOD) con DASH

El consumo de contenido multimedia a través de Internet, especialmente el vídeo, está experimentado un crecimiento constante, convirtiéndose en una actividad cotidiana entre individuos de todo el mundo. En este contexto, en los últimos años se han desarrollado numerosos estudios enfocados en la preparación, distribución y transmisión de contenido multimedia, especialmente en el ámbito del vídeo bajo demanda (VoD). Esta tesis propone diferentes contribuciones en el campo de la codificación de vídeo para VoD que será transmitido usando el estándar Dynamic Adaptive Streaming over HTTP (DASH). El objetivo es encontrar un equilibrio entre el uso eficiente de recursos computacionales y la garantía de ofrecer una calidad experiencia (QoE) alta para el espectador final. Como punto de partida, se ofrece un estudio exhaustivo sobre investigaciones relacionadas con técnicas de codificación y transcodificación de vídeo en la nube, enfocándose especialmente en la evolución del streaming y la relevancia del proceso de codificación. Además, se examinan las propuestas en función del tipo de virtualización y modalidades de entrega de contenido. Se desarrollan dos enfoques de codificación adaptativa basada en la calidad, con el objetivo de ajustar la calidad de toda la secuencia de vídeo a un nivel deseado. Los resultados indican que las soluciones propuestas pueden reducir el tamaño del vídeo manteniendo la misma calidad a lo largo de todos los segmentos del vídeo. Además, se propone una solución de codificación basada en escenas y se analiza el impacto de utilizar vídeo a baja resolución (downscaling) para detectar escenas en términos de tiempo, calidad y tamaño. Los resultados muestran que se reduce el tiempo total de codificación, el consumo de recursos computacionales y el tamaño del vídeo codificado. La investigación también presenta una arquitectura que paraleliza los trabajos involucrados en la preparación de contenido DASH utilizando el paradigma FaaS (Function-as-a-Service), en una plataforma serverless. Se prueba esta arquitectura con tres funciones encapsuladas en contenedores, para codificar y analizar la calidad de los vídeos, obteniendo resultados prometedores en términos de escalabilidad y distribución de trabajos. Finalmente, se crea una herramienta llamada VQMTK, que integra 14 métricas de calidad de vídeo en un contenedor con Docker, facilitando la evaluación de la calidad del vídeo en diversos entornos. Esta herramienta puede ser de gran utilidad en el ámbito de la codificación de vídeo, en la generación de conjuntos de datos para entrenar redes neuronales profundas y en entornos científicos como educativos. En resumen, la tesis ofrece soluciones y herramientas innovadoras para mejorar la eficiencia y la calidad en la preparación y transmisión de contenido multimedia en la nube, proporcionando una base sólida para futuras investigaciones y desarrollos en este campo que está en constante evolución.The consumption of multimedia content over the Internet, especially video, is growing steadily, becoming a daily activity among people around the world. In this context, several studies have been developed in recent years focused on the preparation, distribution, and transmission of multimedia content, especially in the field of video on demand (VoD). This thesis proposes different contributions in the field of video coding for transmission in VoD scenarios using Dynamic Adaptive Streaming over HTTP (DASH) standard. The goal is to find a balance between the efficient use of computational resources and the guarantee of delivering a high-quality experience (QoE) for the end viewer. As a starting point, a comprehensive survey on research related to video encoding and transcoding techniques in the cloud is provided, focusing especially on the evolution of streaming and the relevance of the encoding process. In addition, proposals are examined as a function of the type of virtualization and content delivery modalities. Two quality-based adaptive coding approaches are developed with the objective of adjusting the quality of the entire video sequence to a desired level. The results indicate that the proposed solutions can reduce the video size while maintaining the same quality throughout all video segments. In addition, a scene-based coding solution is proposed and the impact of using downscaling video to detect scenes in terms of time, quality and size is analyzed. The results show that the required encoding time, computational resource consumption and the size of the encoded video are reduced. The research also presents an architecture that parallelizes the jobs involved in content preparation using the FaaS (Function-as-a-Service) paradigm, on a serverless platform. This architecture is tested with three functions encapsulated in containers, to encode and analyze the quality of the videos, obtaining promising results in terms of scalability and job distribution. Finally, a tool called VQMTK is developed, which integrates 14 video quality metrics in a container with Docker, facilitating the evaluation of video quality in various environments. This tool can be of great use in the field of video coding, in the generation of datasets to train deep neural networks, and in scientific environments such as educational. In summary, the thesis offers innovative solutions and tools to improve efficiency and quality in the preparation and transmission of multimedia content in the cloud, providing a solid foundation for future research and development in this constantly evolving field