QoE for Mobile Streaming

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Identifying and diagnosing video streaming performance issues

On-line video streaming is an ever evolving ecosystem of services and technologies, where content providers are on a constant race to satisfy the users' demand for richer content and higher bitrate streams, updated set of features and cross-platform compatibility. At the same time, network operators are required to ensure that the requested video streams are delivered through the network with a satisfactory quality in accordance with the existing Service Level Agreements (SLA). However, tracking and maintaining satisfactory video Quality of Experience (QoE) has become a greater challenge for operators than ever before. With the growing popularity of content engagement on handheld devices and over wireless connections, new points-of-failure have added to the list of failures that can affect the video quality. Moreover, the adoption of end-to-end encryption by major streaming services has rendered previously used QoE diagnosis methods obsolete. In this thesis, we identify the current challenges in identifying and diagnosing video streaming issues and we propose novel approaches in order to address them. More specifically, the thesis initially presents methods and tools to identify a wide array of QoE problems and the severity with which they affect the users' experience. The next part of the thesis deals with the investigation of methods to locate under-performing parts of the network that lead to drop of the delivered quality of a service. In this context, we propose a data-driven methodology for detecting the under performing areas of cellular network with sub-optimal Quality of Service (QoS) and video QoE. Moreover, we develop and evaluate a multi-vantage point framework that is capable of diagnosing the underlying faults that cause the disruption of the user's experience. The last part of this work, further explores the detection of network performance anomalies and introduces a novel method for detecting such issues using contextual information. This approach provides higher accuracy when detecting network faults in the presence of high variation and can benefit providers to perform early detection of anomalies before they result in QoE issues.La distribución de vídeo online es un ecosistema de servicios y tecnologías, donde los proveedores de contenidos se encuentran en una carrera continua para satisfacer las demandas crecientes de los usuarios de más riqueza de contenido, velocidad de transmisión, funcionalidad y compatibilidad entre diferentes plataformas. Asimismo, los operadores de red deben asegurar que los contenidos demandados son entregados a través de la red con una calidad satisfactoria según los acuerdos existentes de nivel de servicio (en inglés Service Level Agreement o SLA). Sin embargo, la monitorización y el mantenimiento de un nivel satisfactorio de la calidad de experiencia (en inglés Quality of Experience o QoE) del vídeo online se ha convertido en un reto mayor que nunca para los operadores. Dada la creciente popularidad del consumo de contenido con dispositivos móviles y a través de redes inalámbricas, han aparecido nuevos puntos de fallo que se han añadido a la lista de problemas que pueden afectar a la calidad del vídeo transmitido. Adicionalmente, la adopción de sistemas de encriptación extremo a extremo, por parte de los servicios más importantes de distribución de vídeo online, ha dejado obsoletos los métodos existentes de diagnóstico de la QoE. En esta tesis se identifican los retos actuales en la identificación y diagnóstico de los problemas de transmisión de vídeo online, y se proponen nuevas soluciones para abordar estos problemas. Más concretamente, inicialmente la tesis presenta métodos y herramientas para identificar un conjunto amplio de problemas de QoE y la severidad con los que estos afectan a la experiencia de los usuarios. La siguiente parte de la tesis investiga métodos para localizar partes de la red con un rendimiento bajo que resultan en una disminución de la calidad del servicio ofrecido. En este contexto, se propone una metodología basada en el análisis de datos para detectar áreas de la red móvil que ofrecen un nivel subóptimo de calidad de servicio (en inglés Quality of Service o QoS) y QoE. Además, se desarrolla y se evalúa una solución basada en múltiples puntos de medida que es capaz de diagnosticar los problemas subyacentes que causan la alteración de la experiencia de usuario. La última parte de este trabajo explora adicionalmente la detección de anomalías de rendimiento de la red y presenta un nuevo método para detectar estas situaciones utilizando información contextual. Este enfoque proporciona una mayor precisión en la detección de fallos de la red en presencia de alta variabilidad y puede ayudar a los proveedores a la detección precoz de anomalías antes de que se conviertan en problemas de QoE.La distribució de vídeo online és un ecosistema de serveis i tecnologies, on els proveïdors de continguts es troben en una cursa continua per satisfer les demandes creixents del usuaris de més riquesa de contingut, velocitat de transmissió, funcionalitat i compatibilitat entre diferents plataformes. A la vegada, els operadors de xarxa han d’assegurar que els continguts demandats són entregats a través de la xarxa amb una qualitat satisfactòria segons els acords existents de nivell de servei (en anglès Service Level Agreement o SLA). Tanmateix, el monitoratge i el manteniment d’un nivell satisfactori de la qualitat d’experiència (en anglès Quality of Experience o QoE) del vídeo online ha esdevingut un repte més gran que mai per als operadors. Donada la creixent popularitat del consum de contingut amb dispositius mòbils i a través de xarxes sense fils, han aparegut nous punts de fallada que s’han afegit a la llista de problemes que poden afectar a la qualitat del vídeo transmès. Addicionalment, l’adopció de sistemes d’encriptació extrem a extrem, per part dels serveis més importants de distribució de vídeo online, ha deixat obsolets els mètodes existents de diagnòstic de la QoE. En aquesta tesi s’identifiquen els reptes actuals en la identificació i diagnòstic dels problemes de transmissió de vídeo online, i es proposen noves solucions per abordar aquests problemes. Més concretament, inicialment la tesi presenta mètodes i eines per identificar un conjunt ampli de problemes de QoE i la severitat amb la que aquests afecten a la experiència dels usuaris. La següent part de la tesi investiga mètodes per localitzar parts de la xarxa amb un rendiment baix que resulten en una disminució de la qualitat del servei ofert. En aquest context es proposa una metodologia basada en l’anàlisi de dades per detectar àrees de la xarxa mòbil que ofereixen un nivell subòptim de qualitat de servei (en anglès Quality of Service o QoS) i QoE. A més, es desenvolupa i s’avalua una solució basada en múltiples punts de mesura que és capaç de diagnosticar els problemes subjacents que causen l’alteració de l’experiència d’usuari. L’última part d’aquest treball explora addicionalment la detecció d’anomalies de rendiment de la xarxa i presenta un nou mètode per detectar aquestes situacions utilitzant informació contextual. Aquest enfoc proporciona una major precisió en la detecció de fallades de la xarxa en presencia d’alta variabilitat i pot ajudar als proveïdors a la detecció precoç d’anomalies abans de que es converteixin en problemes de QoE.Postprint (published version

Metrics for Broadband Networks in the Context of the Digital Economies

In a transition to automated digital management of broadband networks, communication service providers must look for new metrics to monitor these networks. Complete metrics frameworks are already emerging whereas majority of the new metrics are being proposed in technical papers. Considering common metrics for broadband networks and related technologies, this chapter offers insights into what metrics are available, and also suggests active areas of research. The broadband networks being a key component of the digital ecosystems are also an enabler to many other digital technologies and services. Reviewing first the metrics for computing systems, websites and digital platforms, the chapter focus then shifts to the most important technical and business metrics which are used for broadband networks. The demand-side and supply-side metrics including the key metrics of broadband speed and broadband availability are touched on. After outlining the broadband metrics which have been standardized and the metrics for measuring Internet traffic, the most commonly used metrics for broadband networks are surveyed in five categories: energy and power metrics, quality of service, quality of experience, security metrics, and robustness and resilience metrics. The chapter concludes with a discussion on machine learning, big data and the associated metrics

Subjective and Objective Quality-of-Experience of Adaptive Video Streaming

With the rapid growth of streaming media applications, there has been a strong demand of Quality-of-Experience (QoE) measurement and QoE-driven video delivery technologies. While the new worldwide standard dynamic adaptive streaming over hypertext transfer protocol (DASH) provides an inter-operable solution to overcome the volatile network conditions, its complex characteristic brings new challenges to the objective video QoE measurement models. How streaming activities such as stalling and bitrate switching events affect QoE is still an open question, and is hardly taken into consideration in the traditionally QoE models. More importantly, with an increasing number of objective QoE models proposed, it is important to evaluate the performance of these algorithms in a comparative setting and analyze the strengths and weaknesses of these methods. In this study, we build two subject-rated streaming video databases. The progressive streaming video database is dedicated to investigate the human responses to the combined effect of video compression, initial buffering, and stalling. The adaptive streaming video database is designed to evaluate the performance of adaptive bitrate streaming algorithms and objective QoE models. We also provide useful insights on the improvement of adaptive bitrate streaming algorithms. Furthermore, we propose a novel QoE prediction approach to account for the instantaneous quality degradation due to perceptual video presentation impairment, the playback stalling events, and the instantaneous interactions between them. Twelve QoE algorithms from four categories including signal fidelity-based, network QoS-based, application QoS-based, and hybrid QoE models are assessed in terms of correlation with human perception on the two streaming video databases. Experimental results show that the proposed model is in close agreement with subjective opinions and significantly outperforms traditional QoE models

Analyse intelligente de la qualité d'expérience (QoE) dans les réseaux de diffusion de contenu web et mutimédia

Today user experience is becoming a reliable indicator for service providers and telecommunication operators to convey overall end to end system functioning. Moreover, to compete for a prominent market share, different network operators and service providers should retain and increase the customers’ subscription. To fulfil these requirements they require an efficient Quality of Experience (QoE) monitoring and estimation. However, QoE is a subjective metric and its evaluation is expensive and time consuming since it requires human participation. Therefore, there is a need for an objective tool that can measure the QoE objectively with reasonable accuracy in real-Time. As a first contribution, we analyzed the impact of network conditions on Video on Demand (VoD) services. We also proposed an objective QoE estimation tool that uses fuzzy expert system to estimate QoE from network layer QoS parameters. As a second contribution, we analyzed the impact of MAC layer QoS parameters on VoD services over IEEE 802.11n wireless networks. We also proposed an objective QoE estimation tool that uses random neural network to estimate QoE from the MAC layer perspective. As our third contribution, we analyzed the effect of different adaption scenarios on QoE of adaptive bit rate streaming. We also developed a web based subjective test platform that can be easily integrated in a crowdsourcing platform for performing subjective tests. As our fourth contribution, we analyzed the impact of different web QoS parameters on web service QoE. We also proposed a novel machine learning algorithm i.e. fuzzy rough hybrid expert system for estimating web service QoE objectivelyDe nos jours, l’expérience de l'utilisateur appelé en anglais « User Experience » est devenue l’un des indicateurs les plus pertinents pour les fournisseurs de services ainsi que pour les opérateurs de télécommunication pour analyser le fonctionnement de bout en bout de leurs systèmes (du terminal client, en passant par le réseaux jusqu’à l’infrastructure des services etc.). De plus, afin d’entretenir leur part de marché et rester compétitif, les différents opérateurs de télécommunication et les fournisseurs de services doivent constamment conserver et accroître le nombre de souscription des clients. Pour répondre à ces exigences, ils doivent disposer de solutions efficaces de monitoring et d’estimation de la qualité d'expérience (QoE) afin d’évaluer la satisfaction de leur clients. Cependant, la QoE est une mesure qui reste subjective et son évaluation est coûteuse et fastidieuse car elle nécessite une forte participation humaine (appelé panel de d’évaluation). Par conséquent, la conception d’un outil qui peut mesurer objectivement cette qualité d'expérience avec une précision raisonnable et en temps réel est devenue un besoin primordial qui constitue un challenge intéressant à résoudre. Comme une première contribution, nous avons analysé l'impact du comportement d’un réseau sur la qualité des services de vidéo à la demande (VOD). Nous avons également proposé un outil d'estimation objective de la QoE qui utilise le système expert basé sur la logique floue pour évaluer la QoE à partir des paramètres de qualité de service de la couche réseau. Dans une deuxième contribution, nous avons analysé l'impact des paramètres QoS de couche MAC sur les services de VoD dans le cadre des réseaux sans fil IEEE 802.11n. Nous avons également proposé un outil d'estimation objective de la QoE qui utilise le réseau aléatoire de neurones pour estimer la QoE dans la perspective de la couche MAC. Pour notre troisième contribution, nous avons analysé l'effet de différents scénarios d'adaptation sur la QoE dans le cadre du streaming adaptatif au débit. Nous avons également développé une plate-Forme Web de test subjectif qui peut être facilement intégré dans une plate-Forme de crowd-Sourcing pour effectuer des tests subjectifs. Finalement, pour notre quatrième contribution, nous avons analysé l'impact des différents paramètres de qualité de service Web sur leur QoE. Nous avons également proposé un algorithme d'apprentissage automatique i.e. un système expert hybride rugueux basé sur la logique floue pour estimer objectivement la QoE des Web service

Video Quality Prediction for Video over Wireless Access Networks (UMTS and WLAN)

Transmission of video content over wireless access networks (in particular, Wireless Local Area Networks (WLAN) and Third Generation Universal Mobile Telecommunication System (3G UMTS)) is growing exponentially and gaining popularity, and is predicted to expose new revenue streams for mobile network operators. However, the success of these video applications over wireless access networks very much depend on meeting the user’s Quality of Service (QoS) requirements. Thus, it is highly desirable to be able to predict and, if appropriate, to control video quality to meet user’s QoS requirements. Video quality is affected by distortions caused by the encoder and the wireless access network. The impact of these distortions is content dependent, but this feature has not been widely used in existing video quality prediction models. The main aim of the project is the development of novel and efficient models for video quality prediction in a non-intrusive way for low bitrate and resolution videos and to demonstrate their application in QoS-driven adaptation schemes for mobile video streaming applications. This led to five main contributions of the thesis as follows:(1) A thorough understanding of the relationships between video quality, wireless access network (UMTS and WLAN) parameters (e.g. packet/block loss, mean burst length and link bandwidth), encoder parameters (e.g. sender bitrate, frame rate) and content type is provided. An understanding of the relationships and interactions between them and their impact on video quality is important as it provides a basis for the development of non-intrusive video quality prediction models.(2) A new content classification method was proposed based on statistical tools as content type was found to be the most important parameter. (3) Efficient regression-based and artificial neural network-based learning models were developed for video quality prediction over WLAN and UMTS access networks. The models are light weight (can be implemented in real time monitoring), provide a measure for user perceived quality, without time consuming subjective tests. The models have potential applications in several other areas, including QoS control and optimization in network planning and content provisioning for network/service providers.(4) The applications of the proposed regression-based models were investigated in (i) optimization of content provisioning and network resource utilization and (ii) A new fuzzy sender bitrate adaptation scheme was presented at the sender side over WLAN and UMTS access networks. (5) Finally, Internet-based subjective tests that captured distortions caused by the encoder and the wireless access network for different types of contents were designed. The database of subjective results has been made available to research community as there is a lack of subjective video quality assessment databases.Partially sponsored by EU FP7 ADAMANTIUM Project (EU Contract 214751

3D multiple description coding for error resilience over wireless networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Mobile communications has gained a growing interest from both customers and service providers alike in the last 1-2 decades. Visual information is used in many application domains such as remote health care, video –on demand, broadcasting, video surveillance etc. In order to enhance the visual effects of digital video content, the depth perception needs to be provided with the actual visual content. 3D video has earned a significant interest from the research community in recent years, due to the tremendous impact it leaves on viewers and its enhancement of the user’s quality of experience (QoE). In the near future, 3D video is likely to be used in most video applications, as it offers a greater sense of immersion and perceptual experience. When 3D video is compressed and transmitted over error prone channels, the associated packet loss leads to visual quality degradation. When a picture is lost or corrupted so severely that the concealment result is not acceptable, the receiver typically pauses video playback and waits for the next INTRA picture to resume decoding. Error propagation caused by employing predictive coding may degrade the video quality severely. There are several ways used to mitigate the effects of such transmission errors. One widely used technique in International Video Coding Standards is error resilience. The motivation behind this research work is that, existing schemes for 2D colour video compression such as MPEG, JPEG and H.263 cannot be applied to 3D video content. 3D video signals contain depth as well as colour information and are bandwidth demanding, as they require the transmission of multiple high-bandwidth 3D video streams. On the other hand, the capacity of wireless channels is limited and wireless links are prone to various types of errors caused by noise, interference, fading, handoff, error burst and network congestion. Given the maximum bit rate budget to represent the 3D scene, optimal bit-rate allocation between texture and depth information rendering distortion/losses should be minimised. To mitigate the effect of these errors on the perceptual 3D video quality, error resilience video coding needs to be investigated further to offer better quality of experience (QoE) to end users. This research work aims at enhancing the error resilience capability of compressed 3D video, when transmitted over mobile channels, using Multiple Description Coding (MDC) in order to improve better user’s quality of experience (QoE). Furthermore, this thesis examines the sensitivity of the human visual system (HVS) when employed to view 3D video scenes. The approach used in this study is to use subjective testing in order to rate people’s perception of 3D video under error free and error prone conditions through the use of a carefully designed bespoke questionnaire.Petroleum Technology Development Fund (PTDF

Enabling energy-awareness for internet video

Continuous improvements to the state of the art have made it easier to create, send and receive vast quantities of video over the Internet. Catalysed by these developments, video is now the largest, and fastest growing type of traffic on modern IP networks. In 2015, video was responsible for 70% of all traffic on the Internet, with an compound annual growth rate of 27%. On the other hand, concerns about the growing energy consumption of ICT in general, continue to rise. It is not surprising that there is a significant energy cost associated with these extensive video usage patterns. In this thesis, I examine the energy consumption of typical video configurations during decoding (playback) and encoding through empirical measurements on an experimental test-bed. I then make extrapolations to a global scale to show the opportunity for significant energy savings, achievable by simple modifications to these video configurations. Based on insights gained from these measurements, I propose a novel, energy-aware Quality of Experience (QoE) metric for digital video - the Energy - Video Quality Index (EnVI). Then, I present and evaluate vEQ-benchmark, a benchmarking and measurement tool for the purpose of generating EnVI scores. The tool enables fine-grained resource-usage analyses on video playback systems, and facilitates the creation of statistical models of power usage for these systems. I propose GreenDASH, an energy-aware extension of the existing Dynamic Adaptive Streaming over HTTP standard (DASH). GreenDASH incorporates relevant energy-usage and video quality information into the existing standard. It could enable dynamic, energy-aware adaptation for video in response to energy-usage and user ‘green’ preferences. I also evaluate the subjective perception of such energy-aware, adaptive video streaming by means of a user study featuring 36 participants. I examine how video may be adapted to save energy without a significant impact on the Quality of Experience of these users. In summary, this thesis highlights the significant opportunities for energy savings if Internet users gain an awareness about their energy usage, and presents a technical discussion how this can be achieved by straightforward extensions to the current state of the art