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-

Quality of experience-centric management of adaptive video streaming services : status and challenges

Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming ( HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users' QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users' QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users' QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server-and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years

Look ahead to improve QoE in DASH streaming

[EN] When a video is encoded with constant quality, the resulting bitstream will have variable bitrate due to the inherent nature of the video encoding process. This paper proposes a video Adaptive Bitrate Streaming (ABR) algorithm, called Look Ahead, which takes into account this bitrate variability in order to calculate, in real time, the appropriate quality level that minimizes the number of interruptions during the playback. The algorithm is based on the Dynamic Adaptive Streaming over HTTP (DASH) standard for on-demand video services. In fact, it has been implemented and integrated into ExoPlayer v2, the latest version of the library developed by Google to play DASH contents. The proposed algorithm is compared to the Müller and Segment Aware Rate Adaptation (SARA) algorithms as well as to the default ABR algorithm integrated into ExoPlayer. The comparison is carried out by using the most relevant parameters that affect the Quality of Experience (QoE) in video playback services, that is, number and duration of stalls, average quality of the video playback and number of representation switches. These parameters can be combined to define a QoE model. In this sense, this paper also proposes two new QoE models for the evaluation of ABR algorithms. One of them considers the bitrate of every segment of each representation, and the second is based on VMAF (Video Multimethod Assessment Fusion), a Video Quality Assessment (VQA) method developed by Netflix. The evaluations presented in the paper reflect: first, that Look Ahead outperforms the Müller, SARA and the ExoPlayer ABR algorithms in terms of number and duration of video playback stalls, with hardly decreasing the average video quality; and second, that the two QoE models proposed are more accurate than other similar models existing in the literature.This work is supported by the PAID-10-18 Program of the Universitat Politecnica de Valencia (Ayudas para contratos de acceso al sistema espanol de Ciencia, Tecnologia e Innovacion, en estructuras de investigacion de la Universitat Politecnica de Valencia) and by the Project 20180810 from the Universitat Politecnica de Valencia ("Tecnologias de distribucion y procesado de informacion multimedia y QoE").Belda Ortega, R.; De Fez Lava, I.; Arce Vila, P.; Guerri Cebollada, JC. (2020). Look ahead to improve QoE in DASH streaming. 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Avaliação da qualidade de experiência de vídeo em várias tecnologias

Mestrado em Engenharia Eletrónica e TelecomunicaçõesNowadays the internet is associated with many services. Combined with this fact, there is a marked increase of the users joining this service. In this perspective, it is required that the service providers guarantee a minimum quality to the network services. The Quality of Experience of services is quite crucial in the development of services in networks. Also noteworthy, the tra c increase in multimedia services, including video streaming, increases the probability of congesting the networks. In the perspective of the service provider, the monitoring is a solution to avoid saturation in network. This way, this dissertation proposes to develop a platform that allows a multimedia tra c monitoring in the Meo Go service provided by the operator Portugal Telecom Communications. The architecture of the adaptive streaming over HTTP has been studied and tested to obtain the quality of experience metrics. This adaptive streaming technique presents the smooth streaming, an architecture made by Microsoft company, and it is used in the Meo Go service. Then, it is monitored the metrics obtained with the video player. This analysis is done objectively and subjectively. In this phase, the objective implementation of the method allows to obtain the prediction value of the Quality of Experience by consumers. The selected metrics were derived from the state / performance of network and terminal device. The obtained metrics aim to simulate human action in video score quality. Otherwise, subjectively, it is conducted a survey based in a questionnaire to compare methods. In this phase it was created an on-line platform to allow the obtain a greater number of rankings and data processing. In the obtained results, rstly in the smooth streaming player, it is shown the adaptive streaming implementation technique. On the next phase, test scenarios were created to demonstrate the functioning of the method in many cases, with greater relevance for those ones with higher dynamic complexity. From the perspective of subjective and objective methods, these have values that con rm the architecture of the implemented module. Over time, the performance of the scoring the quality of video streaming services approaches the one in a human mental action.Nos dias de hoje a Internet é um dos meios com mais serviços associados. Conjugado a este facto, existe um acentuado aumento de utilizadores a aderir a este serviço. Nesta perspectiva existe a necessidade de garantir uma qualidade mínima por parte dos prestadores de serviços. A Qualidade de Experiência que os consumidores têm dos serviços é bastante crucial no desenvolvimento e optimização dos serviços nas redes. É ainda de salientar que o aumento do tráfego multimédia, nomeadamente os streamings de vídeo, apresenta incrementos na probabilidade de as redes se congestionarem. Na perspectiva do prestador de serviços a monitorização é a solução para evitar a saturação total. Neste sentido, esta dissertação pretende desenvolver uma plataforma que permite a monitorização do tráfego de multimédia do serviço do Meo Go, fornecido pela operadora Portugal Telecom Comunicações. Neste trabalho foi necessário investigar e testar a arquitectura do streaming adaptativo sobre HTTP para ser possível obter métricas de qualidade de experiência. Este streaming adaptativo apresenta a técnica de smooth streaming, sendo esta arquitectura projectada pela empresa Microsoft e utilizada no serviço Meo Go. Posteriormente foram monitorizadas as métricas que se obtiveram no player de vídeo. Esta análise foi realizada de forma objectiva e subjectiva. Nesta fase da implementação objectiva do método em que se pretende obter uma predição do valor de Qualidade de Experiência por parte do consumidor, foram seleccionadas as métricas oriundas do estado/desempenho da rede e do dispositivo terminal. As métricas obtidas entram num processo de tratamento que pretende simular a ação humana nas classificações da qualidade dos vídeos. De outra forma, subjectivamente, foi realizada uma pesquisa, com base num questionário, de modo a comparar os métodos. Nesta etapa foi gerada uma plataforma online que possibilitou obter um maior número de classificações dos vídeos para posteriormente se proceder ao tratamento de dados. Nos resultados obtidos, primeiramente ao nível do player de smooth streaming, estes permitem analisar a técnica de implementação de streaming adaptativo. Numa fase seguinte foram criados cenários de teste para comprovar o funcionamento do método em diversas situações, tendo com maior relevância aqueles que contêm dinâmicas mais complexas. Na perspectiva dos métodos subjectivo e objectivo, estes apresentam valores que confirmam a arquitectura do módulo implementado. Adicionalmente, o desempenho do método em classificar a qualidade de serviço de vídeo streaming, ao longo do tempo, apresentou valores que se aproximam da dinâmica esperada numa ação mental humana

Measurement And Improvement of Quality-of-Experience For Online Video Streaming Services

Title from PDF of title page, viewed on September 4, 2015Dissertation advisor: Deep MedhiVitaIncludes bibliographic references (pages 126-141)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2015HTTP based online video streaming services have been consistently dominating the online traffic for the past few years. Measuring and improving the performance of these services is an important challenge. Traditional Quality-of-Service (QoS) metrics such as packet loss, jitter and delay which were used for networked services are not easily understood by the users. Instead, Quality-of-Experience (QoE) metrics which capture the overall satisfaction are more suitable for measuring the quality as perceived by the users. However, these QoE metrics have not yet been standardized and their measurement and improvement poses unique challenges. In this work we first present a comprehensive survey of the different set of QoE metrics and the measurement methodologies suitable for HTTP based online video streaming services. We then present our active QoE measurement tool Pytomo that measures the QoE of YouTube videos. A case study on the measurement of QoE of YouTube videos when accessed by residential users from three different Internet Service Providers (ISP) in a metropolitan area is discussed. This is the first work that has collected QoE data from actual residential users using active measurements for YouTube videos. Based on these measurements we were able to study and compare the QoE of YouTube videos across multiple ISPs. We also were able to correlate the QoE observed with the server clusters used for the different users. Based on this correlation we were able to identify the server clusters that were experiencing diminished QoE. DynamicAdaptive Streaming overHTTP (DASH) is an HTTP based video streaming that enables the video players to adapt the video quality based on the network conditions. We next present a rate adaptation algorithm that improves the QoE of DASH video streaming services that selects the most optimum video quality. With DASH the video server hosts multiple representation of the same video and each representation is divided into small segments of constant playback duration. The DASH player downloads the appropriate representation based on the network conditions, thus, adapting the video quality to match the conditions. Currently deployed Adaptive Bitrate (ABR) algorithms use throughput and buffer occupancy to predict segment fetch times. These algorithms assume that the segments are of equal size. However, due to the encoding schemes employed this assumption does not hold. In order to overcome these limitations, we propose a novel Segment Aware Rate Adaptation algorithm (SARA) that leverages the knowledge of the segment size variations to improve the prediction of segment fetch times. Using an emulated player in a geographically distributed virtual network setup, we compare the performance of SARA with existing ABR algorithms. We demonstrate that SARA helps to improve the QoE of the DASH video streaming with improved convergence time, better bitrate switching performance and better video quality. We also show that unlike the existing adaptation schemes, SARA provides a consistent QoE irrespective of the segment size distributions.Introduction -- Measurement of QoE for Online Video Streaming Services: A Literature Survey -- Pytomo: A Tool for measuring QoE of YouTube Videos -- Case Study: QoE across three Internet Service Providers in a Metropolitan Area -- Adaptive Bitrate Algorithms for DASH -- Segment Aware Rate Adaptation for DASH -- Performance Evaluation of SARA -- Conclusion and Future Research --Appendix A. Sample MPD Fil

Understanding user experience of mobile video: Framework, measurement, and optimization

Since users have become the focus of product/service design in last decade, the term User eXperience (UX) has been frequently used in the field of Human-Computer-Interaction (HCI). Research on UX facilitates a better understanding of the various aspects of the user’s interaction with the product or service. Mobile video, as a new and promising service and research field, has attracted great attention. Due to the significance of UX in the success of mobile video (Jordan, 2002), many researchers have centered on this area, examining users’ expectations, motivations, requirements, and usage context. As a result, many influencing factors have been explored (Buchinger, Kriglstein, Brandt & Hlavacs, 2011; Buchinger, Kriglstein & Hlavacs, 2009). However, a general framework for specific mobile video service is lacking for structuring such a great number of factors. To measure user experience of multimedia services such as mobile video, quality of experience (QoE) has recently become a prominent concept. In contrast to the traditionally used concept quality of service (QoS), QoE not only involves objectively measuring the delivered service but also takes into account user’s needs and desires when using the service, emphasizing the user’s overall acceptability on the service. Many QoE metrics are able to estimate the user perceived quality or acceptability of mobile video, but may be not enough accurate for the overall UX prediction due to the complexity of UX. Only a few frameworks of QoE have addressed more aspects of UX for mobile multimedia applications but need be transformed into practical measures. The challenge of optimizing UX remains adaptations to the resource constrains (e.g., network conditions, mobile device capabilities, and heterogeneous usage contexts) as well as meeting complicated user requirements (e.g., usage purposes and personal preferences). In this chapter, we investigate the existing important UX frameworks, compare their similarities and discuss some important features that fit in the mobile video service. Based on the previous research, we propose a simple UX framework for mobile video application by mapping a variety of influencing factors of UX upon a typical mobile video delivery system. Each component and its factors are explored with comprehensive literature reviews. The proposed framework may benefit in user-centred design of mobile video through taking a complete consideration of UX influences and in improvement of mobile videoservice quality by adjusting the values of certain factors to produce a positive user experience. It may also facilitate relative research in the way of locating important issues to study, clarifying research scopes, and setting up proper study procedures. We then review a great deal of research on UX measurement, including QoE metrics and QoE frameworks of mobile multimedia. Finally, we discuss how to achieve an optimal quality of user experience by focusing on the issues of various aspects of UX of mobile video. In the conclusion, we suggest some open issues for future study

QoE-Based Low-Delay Live Streaming Using Throughput Predictions

Recently, HTTP-based adaptive streaming has become the de facto standard for video streaming over the Internet. It allows clients to dynamically adapt media characteristics to network conditions in order to ensure a high quality of experience, that is, minimize playback interruptions, while maximizing video quality at a reasonable level of quality changes. In the case of live streaming, this task becomes particularly challenging due to the latency constraints. The challenge further increases if a client uses a wireless network, where the throughput is subject to considerable fluctuations. Consequently, live streams often exhibit latencies of up to 30 seconds. In the present work, we introduce an adaptation algorithm for HTTP-based live streaming called LOLYPOP (Low-Latency Prediction-Based Adaptation) that is designed to operate with a transport latency of few seconds. To reach this goal, LOLYPOP leverages TCP throughput predictions on multiple time scales, from 1 to 10 seconds, along with an estimate of the prediction error distribution. In addition to satisfying the latency constraint, the algorithm heuristically maximizes the quality of experience by maximizing the average video quality as a function of the number of skipped segments and quality transitions. In order to select an efficient prediction method, we studied the performance of several time series prediction methods in IEEE 802.11 wireless access networks. We evaluated LOLYPOP under a large set of experimental conditions limiting the transport latency to 3 seconds, against a state-of-the-art adaptation algorithm from the literature, called FESTIVE. We observed that the average video quality is by up to a factor of 3 higher than with FESTIVE. We also observed that LOLYPOP is able to reach a broader region in the quality of experience space, and thus it is better adjustable to the user profile or service provider requirements.Comment: Technical Report TKN-16-001, Telecommunication Networks Group, Technische Universitaet Berlin. This TR updated TR TKN-15-00