103 research outputs found

    A motion control method for a differential drive robot based on human walking for immersive telepresence

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    Abstract. This thesis introduces an interface for controlling Differential Drive Robots (DDRs) for telepresence applications. Our goal is to enhance immersive experience while reducing user discomfort, when using Head Mounted Displays (HMDs) and body trackers. The robot is equipped with a 360° camera that captures the Robot Environment (RE). Users wear an HMD and use body trackers to navigate within a Local Environment (LE). Through a live video stream from the robot-mounted camera, users perceive the RE within a virtual sphere known as the Virtual Environment (VE). A proportional controller was employed to facilitate the control of the robot, enabling to replicate the movements of the user. The proposed method uses chest tracker to control the telepresence robot and focuses on minimizing vection and rotations induced by the robot’s motion by modifying the VE, such as rotating and translating it. Experimental results demonstrate the accuracy of the robot in reaching target positions when controlled through the body-tracker interface. Additionally, it also reveals an optimal VE size that effectively reduces VR sickness and enhances the sense of presence

    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

    Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques

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    Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, traffic control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classification, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. The basic concepts of EVA (e.g., definition, architectures) were not fully elucidated due to the rapid development of this domain. To fill these gaps, we provide a comprehensive survey of the recent efforts on EVA. In this paper, we first review the fundamentals of edge computing, followed by an overview of VA. The EVA system and its enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA.Comment: 31 pages, 13 figure

    Simulating resource management in fog computing systems

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    The fog computing paradigm was introduced to address the new challenges and requirements posed by the Internet of Things (IoT). It extends the cloud to the edge of the network, thereby facilitating processing and storing a massive amount of data where it is created and used. This novel computing paradigm is widely studied in both the academy and the industry, primarily by simulation. Today, a large variety of edge and fog computing simulators exist and are reviewed by several surveys. These reviews, however, mainly focus on high-level comparisons of these simulators and often make contradictory statements, which makes it difficult to assess what studies are feasible with a simulation tool. To address these challenges, we focus on a single state-of-the-art fog simulation tool, iFogSim2. In this paper, we provide an in-depth review of the simulator and examine its model, assumptions, and technical characteristics. Our analysis describes the details of fog resource management mechanisms implemented by iFogSim2 and discusses what it is capable of and where its limitations lie. We construct a case study to assess the tool's suitability for a mobile 5G scenario, namely, road surface weather analysis with smart vehicles. The case study is used to retrieve qualitative results of what is feasible with the tool, and what is not. We demonstrate that iFogSim2 has a valid locality model for the mobile 5G use case, but it is not suitable for experimenting with vehicular fog computing, dynamic placement, server-side service discovery, and load-balancing. In addition, we present a modeling and analytics framework, built for iFogSim2, to improve the simulation software and facilitate future research with the tool

    Simulation of a Tele-Surgery process through a Live Video Streaming service, using Simu5G and Wowza

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    Telematic services that require low latency for real-time applications and that make use of wireless mobile networks are increasingly popular. In the case of Tele-Surgery services that employ Live Video Streaming (LVS), latency times of the order of 1ms are needed. Given the difficulty of implementing real 5G test scenarios that enable this type of service to be characterized, this paper presents an emulation scenario that uses Simu5G to simulate the network, Wowza as a real video server, OBS Studio for transmission and VLC media player for content playback. This emulation scenario makes it possible to modify such parameters as bitrate, bandwidth, frequency and numerology index in order to evaluate different network configurations. By varying these parameters in a controlled way, packet losses are obtained for different bitrate values. The best quality video was obtained with a bitrate of 3000 Kbps.Los servicios telemáticos que requieren baja latencia para aplicaciones en tiempo real y que utilizan las redes móviles inalámbricas son cada vez más populares. El caso del servicio de Tele-Cirugía que emplea la técnica de Live Video Streaming –LVS requiere tiempos de latencia del orden de 1ms. Ante la dificultad de implementar escenarios de prueba reales de 5G que permitan caracterizar este tipo de servicios, se presenta en este trabajo un escenario de emulación que emplea Simu5G para simular la red, Wowza como servidor real de video, OBS Studio para la transmisión y VLC media player para la reproducción del contenido. Este escenario de emulación permite modificar parámetros como bitrate, ancho de banda, frecuencia e índice de numerología; con el objetivo de evaluar diferentes configuraciones de red. Variando de forma controlada los parámetros mencionados, se obtienen las pérdidas de paquetes para diferentes valores de bitrate. De acuerdo a los resultados, y para el escenario de prueba particular, el vídeo con una mejor calidad se obtuvo con un bitrate de 3000 Kbps.

    Livenet: A low-latency video transport network for large-scale live streaming

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    Low-latency live streaming has imposed stringent latency requirements on video transport networks. In this paper we report on the design and operation of the Alibaba low-latency video transport network, LiveNet. LiveNet builds on a flat CDN overlay with a centralized controller for global optimization. As part of this, we present our design of the global routing computation and path assignment, as well as our fast data transmission architecture with fine-grained control of video frames. The performance results obtained from three years of operation demonstrate the effectiveness of LiveNet in improving CDN performance and QoE metrics. Compared with our prior state-of-The-Art hierarchical CDN deployment, LiveNet halves the CDN delay and ensures 98% of views do not experience stalls and that 95% can start playback within 1 second. We further report our experiences of running LiveNet over the last 3 years

    A Survey on Mobile Edge Computing for Video Streaming : Opportunities and Challenges

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    5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive multimedia applications (e.g., ultra high definition video conferencing, 3D and multiview video streaming, crowd-sourced video streaming, cloud gaming, virtual and augmented reality) are becoming more ambitious with high volume and low latency video streams putting strict demands on the already congested networks. Mobile Edge Computing (MEC) is an emerging paradigm that extends cloud computing capabilities to the edge of the network i.e., at the base station level. To meet the latency requirements and avoid the end-to-end communication with remote cloud data centers, MEC allows to store and process video content (e.g., caching, transcoding, pre-processing) at the base stations. Both video on demand and live video streaming can utilize MEC to improve existing services and develop novel use cases, such as video analytics, and targeted advertisements. MEC is expected to reshape the future of video streaming by providing ultra-reliable and low latency streaming (e.g., in augmented reality, virtual reality, and autonomous vehicles), pervasive computing (e.g., in real-time video analytics), and blockchain-enabled architecture for secure live streaming. This paper presents a comprehensive survey of recent developments in MEC-enabled video streaming bringing unprecedented improvement to enable novel use cases. A detailed review of the state-of-the-art is presented covering novel caching schemes, optimal computation offloading, cooperative caching and offloading and the use of artificial intelligence (i.e., machine learning, deep learning, and reinforcement learning) in MEC-assisted video streaming services.publishedVersionPeer reviewe

    Distributed Cloud Gaming Pipeline

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    Since the rise of Cloud infrastructures and the increased accessibility to platforms capable of playing video games, Gaming as a Service (GaaS) has been growing steadily. The aim in this field is to give the players the possibility to play video games anytime anywhere on any device through a streaming service. A lot of effort is being put in the research of new methods to overcome the limits of current Game Engines, built as monolithic entities, and of the QoS of the streaming. This TFM aims to address the former problem by researching and implementing (a part of) an alternative to the monolithic architecture, focused on splitting the engine into independent services. These would then be able to be distributed along the whole cloud continuum depending on the required QoS

    Method and device for live-streaming with opportunistic mobile edge cloud offloading

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    A novel, pervasive approach to disseminating live streaming content combines secure distributed systems, WiFi multicast, erasure coding, source coding and opportunistic offloading using hyperlocal mobile edge clouds. The solution disclosed to the technical problem of disseminating live streaming content without requiring a substantial equipment, planning and deployment of appropriate network infrastructure points offers an 11 fold reduction on the infrastructural WiFi bandwidth usage without having to modify any existing software or firmware stacks while ensuring stream integrity, authorization and authentication
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