438 research outputs found

    Quality of experience and access network traffic management of HTTP adaptive video streaming

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    The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.Die Doktorarbeit beschäftigt sich mit Quality of Experience (QoE) – der subjektiv empfundenen Dienstgüte – von adaptivem HTTP Videostreaming (HAS) und mit Verkehrsmanagement, das in Zugangsnetzwerken eingesetzt werden kann, um die QoE des adaptiven Videostreamings zu verbessern. Zuerst wurde der Einfluss von Adaptionsparameters und der Zeit pro Qualitätsstufe auf die QoE von adaptivem Videostreaming mittels subjektiver Crowdsourcingstudien untersucht. Die Ergebnisse wurden benutzt, um die QoE-optimale Adaptionsstrategie für gegebene Videos und Netzwerkbedingungen zu berechnen. Dies ermöglicht Dienstanbietern von Videostreaming verbesserte Adaptionsstrategien für adaptives Videostreaming zu entwerfen und zu benchmarken. Weiterhin untersuchte die Arbeit Konzepte zum Überwachen von QoE von Videostreaming in der Applikation und im Netzwerk, die von Netzwerkbetreibern im Kreislauf des QoE-bewussten Verkehrsmanagements eingesetzt werden können. Außerdem wurde eine analytische und simulative Leistungsbewertung von QoE-bewusstem Verkehrsmanagement auf einer Engpassverbindung durchgeführt. Schließlich untersuchte diese Arbeit sozialbewusstes Verkehrsmanagement für adaptives Videostreaming mittels WLAN Offloading, also dem Auslagern von mobilen Videoflüssen über WLAN Netzwerke. Es wurde ein Modell für die Verteilung von öffentlichen WLAN Zugangspunkte und eine Plattform für sozialbewusstes Verkehrsmanagement auf privaten, häuslichen WLAN Routern vorgestellt. Abschließend untersuchte eine simulative Leistungsbewertung den Einfluss von WLAN Offloading auf die QoE und den Energieverbrauch von mobilem adaptivem Videostreaming

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

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    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

    Rate-splitting multiple access for non-terrestrial communication and sensing networks

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    Rate-splitting multiple access (RSMA) has emerged as a powerful and flexible non-orthogonal transmission, multiple access (MA) and interference management scheme for future wireless networks. This thesis is concerned with the application of RSMA to non-terrestrial communication and sensing networks. Various scenarios and algorithms are presented and evaluated. First, we investigate a novel multigroup/multibeam multicast beamforming strategy based on RSMA in both terrestrial multigroup multicast and multibeam satellite systems with imperfect channel state information at the transmitter (CSIT). The max-min fairness (MMF)-degree of freedom (DoF) of RSMA is derived and shown to provide gains compared with the conventional strategy. The MMF beamforming optimization problem is formulated and solved using the weighted minimum mean square error (WMMSE) algorithm. Physical layer design and link-level simulations are also investigated. RSMA is demonstrated to be very promising for multigroup multicast and multibeam satellite systems taking into account CSIT uncertainty and practical challenges in multibeam satellite systems. Next, we extend the scope of research from multibeam satellite systems to satellite- terrestrial integrated networks (STINs). Two RSMA-based STIN schemes are investigated, namely the coordinated scheme relying on CSI sharing and the co- operative scheme relying on CSI and data sharing. Joint beamforming algorithms are proposed based on the successive convex approximation (SCA) approach to optimize the beamforming to achieve MMF amongst all users. The effectiveness and robustness of the proposed RSMA schemes for STINs are demonstrated. Finally, we consider RSMA for a multi-antenna integrated sensing and communications (ISAC) system, which simultaneously serves multiple communication users and estimates the parameters of a moving target. Simulation results demonstrate that RSMA is beneficial to both terrestrial and multibeam satellite ISAC systems by evaluating the trade-off between communication MMF rate and sensing Cramer-Rao bound (CRB).Open Acces

    QIBMRMN: Design of a Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks

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    Multimedia networks utilize low-power scalar nodes to modify wakeup cycles of high-performance multimedia nodes, which assists in optimizing the power-to-performance ratios. A wide variety of machine learning models are proposed by researchers to perform this task, and most of them are either highly complex, or showcase low-levels of efficiency when applied to large-scale networks. To overcome these issues, this text proposes design of a Q-learning based iterative sleep-scheduling and fuses these schedules with an efficient hybrid bioinspired multipath routing model for large-scale multimedia network sets. The proposed model initially uses an iterative Q-Learning technique that analyzes energy consumption patterns of nodes, and incrementally modifies their sleep schedules. These sleep schedules are used by scalar nodes to efficiently wakeup multimedia nodes during adhoc communication requests. These communication requests are processed by a combination of Grey Wolf Optimizer (GWO) & Genetic Algorithm (GA) models, which assist in the identification of optimal paths. These paths are estimated via combined analysis of temporal throughput & packet delivery performance, with node-to-node distance & residual energy metrics. The GWO Model uses instantaneous node & network parameters, while the GA Model analyzes temporal metrics in order to identify optimal routing paths. Both these path sets are fused together via the Q-Learning mechanism, which assists in Iterative Adhoc Path Correction (IAPC), thereby improving the energy efficiency, while reducing communication delay via multipath analysis. Due to a fusion of these models, the proposed Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks (QIBMRMN) is able to reduce communication delay by 2.6%, reduce energy consumed during these communications by 14.0%, while improving throughput by 19.6% & packet delivery performance by 8.3% when compared with standard multimedia routing techniques

    Optimising WLANs Power Saving: Context-Aware Listen Interval

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    Energy is a vital resource in wireless computing systems. Despite the increasing popularity of Wireless Local Area Networks (WLANs), one of the most important outstanding issues remains the power consumption caused by Wireless Network Interface Controller (WNIC). To save this energy and reduce the overall power consumption of wireless devices, a number of power saving approaches have been devised including Static Power Save Mode (SPSM), Adaptive PSM (APSM), and Smart Adaptive PSM (SAPSM). However, the existing literature has highlighted several issues and limitations in regards to their power consumption and performance degradation, warranting the need for further enhancements. This thesis proposes a novel Context-Aware Listen Interval (CALI), in which the wireless network interface, with the aid of a Machine Learning (ML) classification model, sleeps and awakes based on the level of network activity of each application. We focused on the network activity of a single smartphone application while ignoring the network activity of applications running simultaneously. We introduced a context-aware network traffic classification approach based on ML classifiers to classify the network traffic of wireless devices in WLANs. Smartphone applications’ network traffic reflecting a diverse array of network behaviour and interactions were used as contextual inputs for training ML classifiers of output traffic, constructing an ML classification model. A real-world dataset is constructed, based on nine smartphone applications’ network traffic, this is used firstly to evaluate the performance of five ML classifiers using cross-validation, followed by conducting extensive experimentation to assess the generalisation capacity of the selected classifiers on unseen testing data. The experimental results further validated the practical application of the selected ML classifiers and indicated that ML classifiers can be usefully employed for classifying the network traffic of smartphone applications based on different levels of behaviour and interaction. Furthermore, to optimise the sleep and awake cycles of the WNIC in accordance with the smartphone applications’ network activity. Four CALI power saving modes were developed based on the classified output traffic. Hence, the ML classification model classifies the new unseen samples into one of the classes, and the WNIC will be adjusted to operate into one of CALI power saving modes. In addition, the performance of CALI’s power saving modes were evaluated by comparing the levels of energy consumption with existing benchmark power saving approaches using three varied sets of energy parameters. The experimental results show that CALI consumes up to 75% less power when compared to the currently deployed power saving mechanism on the latest generation of smartphones, and up to 14% less energy when compared to SAPSM power saving approach, which also employs an ML classifier

    Leveraging Kubernetes in Edge-Native Cable Access Convergence

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    Public clouds provide infrastructure services and deployment frameworks for modern cloud-native applications. As the cloud-native paradigm has matured, containerization, orchestration and Kubernetes have become its fundamental building blocks. For the next step of cloud-native, an interest to extend it to the edge computing is emerging. Primary reasons for this are low-latency use cases and the desire to have uniformity in cloud-edge continuum. Cable access networks as specialized type of edge networks are not exception here. As the cable industry transitions to distributed architectures and plans the next steps to virtualize its on-premise network functions, there are opportunities to achieve synergy advantages from convergence of access technologies and services. Distributed cable networks deploy resource-constrained devices like RPDs and RMDs deep in the edge networks. These devices can be redesigned to support more than one access technology and to provide computing services for other edge tenants with MEC-like architectures. Both of these cases benefit from virtualization. It is here where cable access convergence and cloud-native transition to edge-native intersect. However, adapting cloud-native in the edge presents a challenge, since cloud-native container runtimes and native Kubernetes are not optimal solutions in diverse edge environments. Therefore, this thesis takes as its goal to describe current landscape of lightweight cloud-native runtimes and tools targeting the edge. While edge-native as a concept is taking its first steps, tools like KubeEdge, K3s and Virtual Kubelet can be seen as the most mature reference projects for edge-compatible solution types. Furthermore, as the container runtimes are not yet fully edge-ready, WebAssembly seems like a promising alternative runtime for lightweight, portable and secure Kubernetes compatible workloads

    Llama : Towards Low Latency Live Adaptive Streaming

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    Multimedia streaming, including on-demand and live delivery of content, has become the largest service, in terms of traffic volume, delivered over the Internet. The ever-increasing demand has led to remarkable advancements in multimedia delivery technology over the past three decades, facilitated by the concurrent pursuit of efficient and quality encoding of digital media. Today, the most prominent technology for online multimedia delivery is HTTP Adaptive Streaming (HAS), which utilises the stateless HTTP architecture - allowing for scalable streaming sessions that can be delivered to millions of viewers around the world using Content Delivery Networks. In HAS, the content is encoded at multiple encoding bitrates, and fragmented into segments of equal duration. The client simply fetches the consecutive segments from the server, at the desired encoding bitrate determined by an ABR algorithm which measures the network conditions and adjusts the bitrate accordingly. This method introduces new challenges to live streaming, where the content is generated in real-time, as it suffers from high end-to-end latency when compared to traditional broadcast methods due to the required buffering at client. This thesis aims to investigate low latency live adaptive streaming, focusing on the reduction of the end-to-end latency. We investigate the impact of latency on the performance of ABR algorithms in low latency scenarios by developing a simulation model and testing prominent on-demand adaptation solutions. Additionally, we conduct extensive subjective testing to further investigate the impact of bitrate changes on the perceived Quality of Experience (QoE) by users. Based on these investigations, we design an ABR algorithm suitable for low latency scenarios which can operate with a small client buffer. We evaluate the proposed low latency adaption solution against on-demand ABR algorithms and the state-of-the-art low latency ABR algorithms, under realistic network conditions using a variety of client and latency settings

    Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

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    Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-

    Network and Content Intelligence for 360 Degree Video Streaming Optimization

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    In recent years, 360° videos, a.k.a. spherical frames, became popular among users creating an immersive streaming experience. Along with the advances in smart- phones and Head Mounted Devices (HMD) technology, many content providers have facilitated to host and stream 360° videos in both on-demand and live stream- ing modes. Therefore, many different applications have already arisen leveraging these immersive videos, especially to give viewers an impression of presence in a digital environment. For example, with 360° videos, now it is possible to connect people in a remote meeting in an interactive way which essentially increases the productivity of the meeting. Also, creating interactive learning materials using 360° videos for students will help deliver the learning outcomes effectively. However, streaming 360° videos is not an easy task due to several reasons. First, 360° video frames are 4–6 times larger than normal video frames to achieve the same quality as a normal video. Therefore, delivering these videos demands higher bandwidth in the network. Second, processing relatively larger frames requires more computational resources at the end devices, particularly for end user devices with limited resources. This will impact not only the delivery of 360° videos but also many other applications running on shared resources. Third, these videos need to be streamed with very low latency requirements due their interactive nature. Inability to satisfy these requirements can result in poor Quality of Experience (QoE) for the user. For example, insufficient bandwidth incurs frequent rebuffer- ing and poor video quality. Also, inadequate computational capacity can cause faster battery draining and unnecessary heating of the device, causing discomfort to the user. Motion or cyber–sickness to the user will be prevalent if there is an unnecessary delay in streaming. These circumstances will hinder providing im- mersive streaming experiences to the much-needed communities, especially those who do not have enough network resources. To address the above challenges, we believe that enhancements to the three main components in video streaming pipeline, server, network and client, are essential. Starting from network, it is beneficial for network providers to identify 360° video flows as early as possible and understand their behaviour in the network to effec- tively allocate sufficient resources for this video delivery without compromising the quality of other services. Content servers, at one end of this streaming pipeline, re- quire efficient 360° video frame processing mechanisms to support adaptive video streaming mechanisms such as ABR (Adaptive Bit Rate) based streaming, VP aware streaming, a streaming paradigm unique to 360° videos that select only part of the larger video frame that fall within the user-visible region, etc. On the other end, the client can be combined with edge-assisted streaming to deliver 360° video content with reduced latency and higher quality. Following the above optimization strategies, in this thesis, first, we propose a mech- anism named 360NorVic to extract 360° video flows from encrypted video traffic and analyze their traffic characteristics. We propose Machine Learning (ML) mod- els to classify 360° and normal videos under different scenarios such as offline, near real-time, VP-aware streaming and Mobile Network Operator (MNO) level stream- ing. Having extracted 360° video traffic traces both in packet and flow level data at higher accuracy, we analyze and understand the differences between 360° and normal video patterns in the encrypted traffic domain that is beneficial for effec- tive resource optimization for enhancing 360° video delivery. Second, we present a WGAN (Wesserstien Generative Adversarial Network) based data generation mechanism (namely VideoTrain++) to synthesize encrypted network video traffic, taking minimal data. Leveraging synthetic data, we show improved performance in 360° video traffic analysis, especially in ML-based classification in 360NorVic. Thirdly, we propose an effective 360° video frame partitioning mechanism (namely VASTile) at the server side to support VP-aware 360° video streaming with dy- namic tiles (or variable tiles) of different sizes and locations on the frame. VASTile takes a visual attention map on the video frames as the input and applies a com- putational geometric approach to generate a non-overlapping tile configuration to cover the video frames adaptive to the visual attention. We present VASTile as a scalable approach for video frame processing at the servers and a method to re- duce bandwidth consumption in network data transmission. Finally, by applying VASTile to the individual user VP at the client side and utilizing cache storage of Multi Access Edge Computing (MEC) servers, we propose OpCASH, a mech- anism to personalize the 360° video streaming with dynamic tiles with the edge assistance. While proposing an ILP based solution to effectively select cached variable tiles from MEC servers that might not be identical to the requested VP tiles by user, but still effectively cover the same VP region, OpCASH maximize the cache utilization and reduce the number of requests to the content servers in congested core network. With this approach, we demonstrate the gain in latency and bandwidth saving and video quality improvement in personalized 360° video streaming
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