66 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

    Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches

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    Traditional networking devices support only fixed features and limited configurability. Network softwarization leverages programmable software and hardware platforms to remove those limitations. In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms. This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0. P4 is the most popular technology to implement programmable data planes. However, programmable data planes, and in particular, the P4 technology, emerged only recently. Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking. The research of this thesis focuses on two open issues of programmable data planes. First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet. Second, it enables BIER in high-performance P4 data planes. BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet. The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study. Two more peer-reviewed papers contain additional content that is not directly related to the main results. They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts

    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

    Volume II Acquisition Research Creating Synergy for Informed Change, Thursday 19th Annual Acquisition Research Proceedings

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    ProceedingsApproved for public release; distribution is unlimited

    Evaluation of PD-L1 expression in various formalin-fixed paraffin embedded tumour tissue samples using SP263, SP142 and QR1 antibody clones

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    Background & objectives: Cancer cells can avoid immune destruction through the inhibitory ligand PD-L1. PD-1 is a surface cell receptor, part of the immunoglobulin family. Its ligand PD-L1 is expressed by tumour cells and stromal tumour infltrating lymphocytes (TIL). Methods: Forty-four cancer cases were included in this study (24 triple-negative breast cancers (TNBC), 10 non-small cell lung cancer (NSCLC) and 10 malignant melanoma cases). Three clones of monoclonal primary antibodies were compared: QR1 (Quartett), SP 142 and SP263 (Ventana). For visualization, ultraView Universal DAB Detection Kit from Ventana was used on an automated platform for immunohistochemical staining Ventana BenchMark GX. Results: Comparing the sensitivity of two different clones on same tissue samples from TNBC, we found that the QR1 clone gave higher percentage of positive cells than clone SP142, but there was no statistically significant difference. Comparing the sensitivity of two different clones on same tissue samples from malignant melanoma, the SP263 clone gave higher percentage of positive cells than the QR1 clone, but again the difference was not statistically significant. Comparing the sensitivity of two different clones on same tissue samples from NSCLC, we found higher percentage of positive cells using the QR1 clone in comparison with the SP142 clone, but once again, the difference was not statistically significant. Conclusion: The three different antibody clones from two manufacturers Ventana and Quartett, gave comparable results with no statistically significant difference in staining intensity/ percentage of positive tumour and/or immune cells. Therefore, different PD-L1 clones from different manufacturers can potentially be used to evaluate the PD- L1 status in different tumour tissues. Due to the serious implications of the PD-L1 analysis in further treatment decisions for cancer patients, every antibody clone, staining protocol and evaluation process should be carefully and meticulously validated

    Potentzia domeinuko NOMA 5G sareetarako eta haratago

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    Tesis inglés 268 p. -- Tesis euskera 274 p.During the last decade, the amount of data carried over wireless networks has grown exponentially. Several reasons have led to this situation, but the most influential ones are the massive deployment of devices connected to the network and the constant evolution in the services offered. In this context, 5G targets the correct implementation of every application integrated into the use cases. Nevertheless, the biggest challenge to make ITU-R defined cases (eMBB, URLLC and mMTC) a reality is the improvement in spectral efficiency. Therefore, in this thesis, a combination of two mechanisms is proposed to improve spectral efficiency: Non-Orthogonal Multiple Access (NOMA) techniques and Radio Resource Management (RRM) schemes. Specifically, NOMA transmits simultaneously several layered data flows so that the whole bandwidth is used throughout the entire time to deliver more than one service simultaneously. Then, RRM schemes provide efficient management and distribution of radio resources among network users. Although NOMA techniques and RRM schemes can be very advantageous in all use cases, this thesis focuses on making contributions in eMBB and URLLC environments and proposing solutions to communications that are expected to be relevant in 6G

    Uma abordagem preditiva de DASH QoE baseada em aprendizado de máquina em multi-access edge computing

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    Orientador: Christian Rodolfo Esteve RothenbergDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O tráfego de serviços de vídeo multimídia está crescendo rapidamente nas redes móveis nos últimos anos. Os serviços de vídeo que usam técnicas de Dynamic Adaptive Streaming sobre HTTP (DASH) dominaram o tráfego total da Internet para transportar o tráfego de vídeo. Espera-se que as operadoras de rede móvel (Mobile Network Operators - MNOs) continuem atendendo a essa demanda crescente por tráfego de vídeo suportado por DASH, ao mesmo tempo em que fornecem uma alta qualidade de experiência (Quality of Experience - QoE) aos usuários finais. Além disso, as operadoras precisam ter um conhecimento claro acerca da qualidade de vídeo percebida pelos usuários finais e relacioná-la com o monitoramento em nível de rede, ou com informações de telemetria para identificação de problemas, análise da causa raiz e predição de padrões. Para garantir um gerenciamento de tráfego de rede com reconhecimento de QoE, um pré-requisito é que os MNOs monitorem o tráfego de rede passivamente e realizem medições efetivas de indicadores-chave de desempenho (Key Performance Indicators - KPIs) de QoE, como resoluções, eventos de paralisação, entre outros, que influenciam diretamente a percepção do usuário final. Muitas abordagens da literatura foram propostas para medir os KPIs com o objetivo de fornecer uma qualidade de serviço de vídeo aceitável. A maioria das soluções exige consciência de contexto do usuário final, o que não é viável do ponto de vista do MNO. No entanto, Deep Packet Inspection (DPI), outra solução mais amplamente usada para estimar os KPIs diretamente do tráfego de rede, não é mais uma solução conveniente para as operadoras devido à adoção de criptografia de streaming de vídeo fim-a-fim sobre TCP (HTTPs) e QUIC. Portanto, o aprendizado de máquina (Machine Learning - ML) passou a ser recentemente aceito como uma solução bem reconhecida para estimar KPIs de QoE, analisando os padrões de tráfego criptografados bem como estatísticas como qualidade de serviço (Quality of Service - QoS). Este trabalho apresenta uma abordagem mais refinada e leve, baseada em aprendizado de máquina, denominada Edge QoE Probe, para estimar QoE do usuário final para o serviço de vídeo DASH, monitorando passivamente o tráfego de rede criptografado na borda da rede. Nossa abordagem pode avaliar vários KPIs de QoE, como por exemplo resolução, taxa de bits, proporção de paralisação, entre outros, tanto em tempo real quanto por sessão. Além disso, neste trabalho investigamos o desempenho do vídeo DASH sobre o protocolo de transporte tradicional TCP (HTTPs) e QUIC. Para este propósito, avaliamos experimentalmente diferentes traces de rede celular em um ambiente emulado de alta fidelidade e comparamos o desempenho comportamental de algoritmos Adaptive Bitrate Streaming (ABS) considerando KPIs de QoE sobre TCP (HTTPs) e QUIC. Nossos resultados empíricos mostram que os algoritmos tradicionais de ABS usando QUIC como transporte precisariam alterações específicas para melhorar o desempenho em termos de QoE de vídeo baseados em DASHAbstract: Multimedia video services traffic is rapidly growing in mobile networks in recent years. Video services using Dynamic Adaptive Streaming over HTTP (DASH) techniques have dominated the total internet traffic to carry video traffic. Mobile Network Operators (MNOs) are expected to run on with this growing demand for DASH-supported video traffic while providing a high Quality of Experience (QoE) to the end-users. Besides, operators need to have a crystal notion of video quality perceived by the end-users and correlate them with network-level monitoring or telemetry information for problem identification, root cause analysis, and pattern prediction. To ensure QoE–aware network traffic management, a prerequisite for the MNOs is to monitor the network traffic passively and measure objective QoE Key Performance Indicators (KPIs) (such as resolutions and stalling events) effectively that directly influence end-user subjective feedback. Many literature approaches have been proposed to measure the KPIs aimed to deliver acceptable video service quality. Most of the solutions require end-user awareness, which is not viable from the MNOs' perspective. However, Deep Packet Inspection (DPI), another most widely used solution to estimate the KPIs directly from network traffic, is not a convenient solution anymore for the operators due to the adoption of end-to-end video streaming encryption over TCP (HTTPs) and QUIC transport protocol. Hence, in recent, Machine Learning (ML) has been accepted as a well-recognized solution for estimating QoE KPIs by analyzing the encrypted traffic patterns and statistics as Quality of Service (QoS). This work presents an ML-based lightweight and fine-grained Edge QoE Probe approach to estimate the end-user QoE for DASH video service by passively monitoring the encrypted network traffic on the edge of the network. Our approach can assess numerous QoE KPIs (such as resolution, bit-rate, quality switches, startup delay, and stall ratio) both in a real-time and per-session manner. Moreover, we investigate the DASH video service performance over the traditional TCP (HTTPs) and QUIC transport protocol in this work. For this purpose, we experimentally evaluate different cellular network traces in a high-fidelity emulated testbed and compare the behavioral performance of Adaptive Bitrate Streaming (ABS) algorithms considering QoE KPIs over TCP (HTTPs) and QUIC. Our empirical results show that QUIC suffers from traditional state-of-the-art ABS algorithms' ineffectiveness to improve video streaming performance without specific changesMestradoEngenharia de ComputaçãoMestre em Engenharia ElétricaFuncam
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