803 research outputs found
Random Linear Network Coding for 5G Mobile Video Delivery
An exponential increase in mobile video delivery will continue with the
demand for higher resolution, multi-view and large-scale multicast video
services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a
number of new opportunities for optimizing video delivery across both 5G core
and radio access networks. One of the promising approaches for video quality
adaptation, throughput enhancement and erasure protection is the use of
packet-level random linear network coding (RLNC). In this review paper, we
discuss the integration of RLNC into the 5G NR standard, building upon the
ideas and opportunities identified in 4G LTE. We explicitly identify and
discuss in detail novel 5G NR features that provide support for RLNC-based
video delivery in 5G, thus pointing out to the promising avenues for future
research.Comment: Invited paper for Special Issue "Network and Rateless Coding for
Video Streaming" - MDPI Informatio
Intelligent Algorithm for Enhancing MPEG-DASH QoE in eMBMS
[EN] Multimedia streaming is the most demanding and bandwidth hungry application in today¿s world of Internet. MPEG-DASH as a video technology standard is designed for delivering live or on-demand streams in Internet to deliver best quality content with the fewest dropouts and least possible buffering. Hybrid architecture of DASH and eMBMS has attracted a great attention from the telecommunication industry and multimedia services. It is deployed in response to the immense demand in multimedia traffic. However, handover and limited available resources of the system affected on dropping segments of the adaptive video streaming in eMBMS and it creates an adverse impact on Quality of Experience (QoE), which is creating trouble for service providers and network providers towards delivering the service. In this paper, we derive a case study in eMBMS to approach to provide test measures evaluating MPEG-DASH QoE, by defining the metrics are influenced on QoE in eMBMS such as bandwidth and packet loss then we observe the objective metrics like stalling (number, duration and place), buffer length and accumulative video time. Moreover, we build a smart algorithm to predict rate of segments are lost in multicast adaptive video streaming. The algorithm deploys an estimation decision regards how to recover the lost segments. According to the obtained results based on our proposal algorithm, rate of lost segments is highly decreased by comparing to the traditional approach of MPEG-DASH multicast and unicast for high number of users.This work has been partially supported by the Postdoctoral Scholarship Contratos Postdoctorales UPV 2014 (PAID-10-14) of the Universitat Politècnica de València , by the Programa para la Formación de Personal Investigador (FPI-2015-S2-884) of the Universitat Politècnica de València , by the Ministerio de EconomÃa y Competitividad , through the Convocatoria 2014. Proyectos I+D - Programa Estatal de Investigación CientÃfica y Técnica de Excelencia in the Subprograma Estatal de Generación de Conocimiento , project TIN2014-57991-C3-1-P and through the Convocatoria 2017 - Proyectos I+D+I - Programa Estatal de Investigación, Desarrollo e Innovación, convocatoria excelencia (Project TIN2017-84802-C2-1-P).Abdullah, MT.; Jimenez, JM.; Canovas Solbes, A.; Lloret, J. (2017). Intelligent Algorithm for Enhancing MPEG-DASH QoE in eMBMS. Network Protocols and Algorithms. 9(3-4):94-114. https://doi.org/10.5296/npa.v9i3-4.12573S9411493-
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 vÃdeo por minuto na plataforma, vem trazer problemas à s redes de dados geridas que já existiam, assim como desafios para os resolver. O tráfego de vÃdeo corresponde a 75% de todos os dados transmitidos na Internet. Assim, não só a Internet se tornou o meio de transmissão de vÃdeo ’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 vÃdeo em avanço passÃvel de ser précarregado, de forma a optimizar a eficiência de entrega das redes onde a qualidade de serviço não é possÃvel de ser garantida. O pré-carregamento dos conteúdos é feito no servidor de cache mais próximo do cliente. Para tal, é utilizado um processo analÃtico 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 vÃdeo 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
Context-Aware Adaptive Prefetching for DASH Streaming over 5G Networks
The increasing consumption of video streams and the demand for higher-quality
content drive the evolution of telecommunication networks and the development
of new network accelerators to boost media delivery while optimizing network
usage. Multi-access Edge Computing (MEC) enables the possibility to enforce
media delivery by deploying caching instances at the network edge, close to the
Radio Access Network (RAN). Thus, the content can be prefetched and served from
the MEC host, reducing network traffic and increasing the Quality of Service
(QoS) and the Quality of Experience (QoE). This paper proposes a novel
mechanism to prefetch Dynamic Adaptive Streaming over HTTP (DASH) streams at
the MEC, employing a Machine Learning (ML) classification model to select the
media segments to prefetch. The model is trained with media session metrics to
improve the forecasts with application layer information. The proposal is
tested with Mobile Network Operators (MNOs)' 5G MEC and RAN and compared with
other strategies by assessing cache and player's performance metrics
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