Decoding the Connection: Viewer Experience and Video Quality Through Human-Centered Constructs

User experience has become the most reliable and trustworthy source for service providers to assess system performance. To fulfill customer requirements, service providers require an efficient quality of experience (QoE) estimation model. QoE is a subjective metric that deals with user perception and can vary dramatically due to various factors such as emotions, the degree of annoyance, past experience, and aesthetics. Moreover, subjective QoE evaluation is expensive and time-consuming because of human participation. Therefore, a model is required to objectively measure QoE with reasonable accuracy. In the context of service and network providers fulfilling user requirements, with a reasonable quality of service (QoS), needs to be provided to the relevant services/applications. However, QoS parameters do not reflect subjective opinion of the user accurately. Therefore, it is necessary to compute the mapping or correlation between QoS and QoE. The mapping may help service providers to understand the behavior of the overall network on user experience and efficiently manage the network resources. In this paper, a new feature number of displayed frames impacted (NoDFI) along with a machine learning based model is presented to compute a correlation between QoS and QoE. A publically available dataset is used to represent the correlation between objective QoS parameters and subjective QoE metric. The result of experiments showed that proposed feature NoDFI proved to be a valuable addition when compared with previously proposed models.

In legacy television services, user centric metrics have been used for more than twenty years to evaluate video quality. These subjective assessment metrics are usually obtained using a panel of human evaluators in standard defined methods to measure the impairments caused by a diversity of factors of the Human Visual System (HVS), constituting what is also called Quality of Experience (QoE) metrics. As video services move to IP networks, the supporting distribution platforms and the type of receiving terminals is getting more heterogeneous, when compared with classical video distributions. The flexibility introduced by these new architectures is, at the same time, enabling an increment of the transmitted video quality to higher definitions and is supporting the transmission of video to lower capability terminals, like mobile terminals. In IP Networks, while Quality of Service (QoS) metrics have been consistently used for evaluating the quality of a transmission and provide an objective way to measure the reliability of communication networks for various purposes, QoE metrics are emerging as a solution to address the limitations of conventional QoS measuring when evaluating quality from the service and user point of view. In terms of media, compressed video usually constitutes a very interdependent structure degrading in a non-graceful manner when exposed to Binary Erasure Channels (BEC), like the Internet or wireless networks. Accordingly, not only the type of encoder and its major encoding parameters (e.g. transmission rate, image definition or frame rate) contribute to the quality of a received video, but also QoS parameters are usually a cause for different types of decoding artifacts. As a result of this, several worldwide standard entities have been evaluating new metrics for the subjective assessment of video transmission over IP networks. In this chapter we are especially interested in explaining some of the best practices available to monitor, evaluate and assure good levels of QoE in packet oriented networks for rich media applications like high quality video streaming. For such applications, service requirements are relatively loose or difficult to quantify and therefore specific techniques have to be clearly understood and evaluated. By the mid of the chapter the reader should have understood why even networks with excellent QoS parameters might have QoE issues, as QoE is a systemic approach that does not relate solely to QoS but to the ensemble of components composing the communication system.

The measurement and evaluation of the QoE (Quality of Experience) have become one of the main focuses in the telecommunications to provide services with the expected quality for their users. However, factors like the network parameters and codification can affect the quality of video, limiting the correlation between the objective and subjective metrics. The above increases the complexity to evaluate the real quality of video perceived by users. In this paper, a model based on artificial neural networks such as BPNNs (Backpropagation Neural Networks) and the RNNs (Random Neural Networks) is applied to evaluate the subjective quality metrics MOS (Mean Opinion Score) and the PSNR (Peak Signal Noise Ratio), SSIM (Structural Similarity Index Metric), VQM (Video Quality Metric), and QIBF (Quality Index Based Frame). The proposed model allows establishing the QoS (Quality of Service) based in the strategyDiffserv. The metrics were analyzed through Pearson’s and Spearman’s correlation coefficients, RMSE (Root Mean Square Error), and outliers rate. Correlation values greater than 90% were obtained for all the evaluated metrics.

Videoconferencing technology is a successful tool for expanding possibilities for collaborative and distance learning, while bridging the distance between the teacher and students, providing time and cost savings. Recently, the focus in literature and practice for quality requirements are shifting from deterministic behavior of the infrastructure in videoconferencing learning environments to students’ Quality of Experience, as subjective measure that involves human dimensions. Hence, this study evaluates the impact of different Quality of Service mechanisms utilized in the infrastructure on students’ Quality of Experience in videoconferencing learning environments. It involved 263 faculty students that participated in 42 learning sessions via videoconferencing during their academic activities, while the infrastructure was subjected to Quality of Service mechanism in the network, as well as application enhancement in the videoconferencing platform, or both. The performance counters from the technical equipment and results from the survey regarding students’ perceived experience, showed definite Quality of Service to Quality of Experience correlation. When network and application Quality of Service were considered complementary, students’ Quality of Experience was in average 18.5% higher compared to network and 15% to application Quality of Service implementations. Similarly, best technical performance was achieved when both mechanisms were consider as a whole, such as 34% decrease in average transmit delay compared to application and 62.5% to network Quality of Service mechanisms, etc. Finally, application controls had greater impact on perceived students’ Quality of Experience than the network ones, which correlated to performance behavior of the infrastructure.Videoconferencing technology is a successful tool for expanding possibilities for collaborative and distance learning, while bridging the distance between the teacher and students, providing time and cost savings. Recently, the focus in literature and practice for quality requirements are shifting from deterministic behavior of the infrastructure in videoconferencing learning environments, or quality of service (QoS), to students’ quality of experience (QoE), as subjective measure that involves human dimensions. Hence, this study evaluates the impact of different QoS mechanisms utilized in the infrastructure on students’ QoE in videoconferencing learning environments. It involved 263 faculty students that participated in 42 learning sessions via videoconferencing during their academic activities, while the infrastructure was subjected to QoS mechanism in the network (NQoS), as well as application enhancement in the videoconferencing platform (AQoS), or both. The performance counters from the technical equipment and results from the survey regarding students’ perceived QoE after each learning session, showed definite QoS/QoE correlation. Even though students’ were not aware of the technical setup during the learning sessions, the highest level of students’ QoE was achieved when NQoS and AQoS were considered complementary, rather than as a single mechanism. In addition, AQoS controls had greater impact on perceived students’ QoE than NQoS.

En los últimos años, el consumo de servicios multimedia ha aumentado y se prevé que esta tendencia continúe en un futuro próximo, convirtiendo el tema de la evaluación de la Calidad de la Experiencia (QoE) en un tema muy importante para valorar el servicio de los proveedores. En este sentido, la optimización de la QoE recibe cada vez más atención ya que las soluciones actuales no han tenido en cuenta, la adaptación, la viabilidad, la rentabi-lidad y la fiabilidad.\nLa presente memoria se centra en la caracterización, diseño, desarrollo y evaluación de diferentes aplicaciones multimedia, con el fin de optimizar la QoE.\n Por tanto, este trabajo investiga la influencia que la infraestructura de redes, las características de los videos y los terminales de los usuarios, presentan en la QoE de los servicios multimedia actuales en Internet. Esta tesis se basa en la investigación exhaustiva de la evaluación subjetiva y objetiva de QoE en redes heterogéneas. Los desafíos y cuestiones relacionados con el estado de la técnica y se discuten en esta disertación.\n En la primera fase, diseñamos una metodología de prueba para evaluar la QoE en la transmisión de video en directo y a través de plataformas de video bajo demanda en redes Wi-Fi y celulares. A partir de esta fase inicial, propondremos los problemas a investigar y las preguntas para resolver a lo largo de esta disertación. Nuestra metodología hace uso de métricas subjetivas y objetivas para evaluar la QoE percibida por los usuarios finales. Se realiza un conjunto de experimentos en laboratorio donde nuestra metodología de pruebas es aplicada. Los resultados obtenidos se recopilan y analizan para extraer las relaciones entre la Calidad de servicio (QoS) y QoE. A partir de estos resultados, se propone un mapeo de QoS-QoE que permite predecir la QoE.\nEn la siguiente fase de la investigación, desarrollamos los algoritmos de optimización de QoE basados en la administración del sistema de red para redes Wi-Fi y celulares. Los algoritmos usan los parámetros clave que se tuvieron en cuenta para la evaluación de QoE. El objetivo de estos algorit-mos es proporcionar un sistema de gestión flexible para las redes con el ob-jetivo de lograr un equilibrio controlado entre la maximización de QoE y la eficiencia del uso de los recursos.\nPor último, se diseña el banco de pruebas del sistema para evaluar el rendimiento de las aplicaciones de servicios multimedia genéricos en los diferentes entornos de prueba. El banco de pruebas del sistema se basa en el enfoque de virtualización; usa los recursos compartidos de un hardware fí-sico para virtualizar todos los componentes. El banco de pruebas virtualiza-do proporciona funciones de red virtualizadas para diferentes escenarios, como Internet (las redes de distribución de contenido - CDNs) y redes inalámbricas. Por lo tanto, se adoptan protocolos livianos y mecanismos ágiles en el sistema, para proporcionar un mejor servicio a los usuarios fina-les. Los resultados de QoE son proporcionados a los proveedores de servi-cios de acuerdo con los parámetros que se definen en el proceso de la eva-luación. Como resultado hemos obtenido un sistema que presenta un servi-cio rentable como una forma factible para la evaluación de la prueba.

Das Internet hat sich zu einem elementaren Bestandteil im Leben von Millionen von Menschen und Unternehmen entwickelt. Im Zuge dieser Entwicklung verschiebt sich die Datenverarbeitung und -speicherung zunehmend in die Cloud (z.B. Google Apps oder Cloud-Spiele), was die Abhängigkeit vom Internet erhöht. Trotz der elementaren Bedeutung des Internets sind dessen Dienste anfällig für Dienstqualitätsprobleme. Ein Einflussfaktor dabei sind Puffer auf verschiedenen Protokollebenen. Diese Dissertation untersucht den Einfluss dieser Puffer auf die Nutzerzufriedenheit (Quality of Experience, QoE). Dies gestaltet sich als Herausforderung, da die Zufriedenheit ein subjektives Maß ist. Diese Dissertation verfolgt daher einen interdisziplinären Ansatz, der QoE- und Netzwerkforschung zur Untersuchung von Puffern auf der Netzwerk- und Anwendungsebene kombiniert. Auf der Netzwerkebene finden sich Internetweit Puffer in Hosts, Switches und Routern. Sie können die Dienstgüte durch Verzögerungen, Jitter und Paketverluste beeinflussen. Eine erste Untersuchung illustriert die negative Auswirkungen solcher Verluste auf die Video QoE. Qualitätsverbesserungen können hierbei durch den Einsatz von Scalable Video Coding erzielt werden. Eine Untersuchung der SVC-Skalierungsdimensionen zeigt, dass spatial scalability zu besseren QoE Vorhersagen führt, als temporal scalability. Eine weitere Studie untersucht den QoE-Einfluss modellbasierter Paketverlustgeneratoren, die beispielsweise in QoE-Studien Verwendung finden. Es wird gezeigt, dass die Modellwahl die QoE-Ergebnisse beeinflusst. Die Puffergröße beeinflusst die entstehende Verzögerung, Jitter und die Paketverlustrate. Trotz jahrzentelanger Forschung und operativer Erfahrung wird die "richtige" Pufferdimensionierung kontrovers diskutiert. Diese Arbeit präsentiert die erste umfassende Studie über den Einfluss der Pufferdimensionierung auf die QoE von Internetanwendungen wie Telefonie, Videostreaming und Webbrowsing. Während überdimensionierte Puffer die QoE beinträchtigen können, beeinflusst die Dimensionierung nach Standardregeln zwar QoS-Metriken, jedoch QoE nur marginal. Auf der Anwendungsebenen kompensieren zusätzliche Puffer Leistungsschwankungen, die z.B. aus Netzwerkpuffern resultieren. Ein weit verbreiteter IPTV-Dienst nutzt solche Puffer, um Verluste mittels einem proprietären Retransmissionprotokoll zu kompensieren und die Videoqualität zu steigern. Eine Studie gibt Einblicke in die Funktionsweise dieses Protokolls und motiviert dadurch die Erweiterung von QoE Metriken, die solche Korrekturmaßnahmen üblicherweise nicht vorsehen und daher zu Fehlabschätzungen führen können. Zur Optimierung von Web QoE untersucht eine weitere Studie die Trefferraten von Cachingverfahren unter Berücksichtigung von YouTube Abfragehäufigkeiten. Dabei wird ein optimiertes Verfahren diskutiert, dass höhere Trefferraten ermöglichen kann, als bei herkömmliche LRU-Caches. Abschließend betrachtet die Dissertation E-Mail Spam als einen relevanten QoE Einflussfaktor. In dieser Studie wird Address Harvesting als Ursache von Spam und verschiedene Mechanismen zur Spamvermeidung, mit dem Ziel der E-Mail QoE Optimierung, über einen Zeitraum von 3,5 Jahren untersucht.

Maintaining quality of streaming video is challenged by network faults resulting into freezes and rebufferings on the video. On top of the network effects, device features have impacts on the image of the video frames displayed during streaming. Despite the simultaneous impacts of video quality from network and device, previous studies considered individual impact of network parameters or devices as influencing factors to propose Quality of Experience (QoE) models. This study proposed QoE model by mapping combined effects from both network and device on video streamed QoE. An experiment to study the effects of video quality from combined effects of network and device over the wireless involved 35 subjects. Combination of packet loss, packet reordering, and delay were emulated using network emulator following Design of Experiment methodology. Through analysis of variance, the study found that packet loss had the highest impact, followed by device features, reordering, and delay on the video QoE. From the combined effects, two-way interactions and three-way interactions had significant effects on the video QoE. Through additive and linearity behavior of the input factors from network and device on video streaming QoE, a multi-factor model was derived. Keywords: Design of Experiment (DOE); Mean Opinion Score (MOS); Quality of Experience (QoE); Quality of Service (QoS); Video Quality Assessment

The concepts of QoE (quality of experience), QoS (quality of service), and GoS (goal of service) are commonly discussed in network performance and user satisfaction studies. Although QoE has become a popular topic in research, the boundaries between QoS and QoE are often blurred, making it difficult to clearly define them. This paper examines the differences and relationships between these terms with regard to their practical applications. QoS is a subjective metric that reflects how users perceive a service. It is influenced by personal preferences and various environmental factors. GoS measures the probability of a successful connection or call under certain conditions. The results showed that implementing QoS features such as traffic prioritization can positively affect both GoS and QoE by reducing packet loss and improving service reliability. It is shown how network performance management using QoS tools can improve user experience and overall service reliability, providing a clearer understanding of how these concepts interact in practice.

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.

Online learning tools are widely used in engineering education. This includes traditional face-to-face, but also distance education. Since these tools rely on Internet connections, the performance of those connections (speed, latency) can impact on how learning tools are experienced by students. Quality of Service (QoS) describes technical performance parameters that reflect the quality of an Internet connection. Quality of Experience (QoE) on the other hand has been widely used to describe how users experience a particular service. In the context of this work, users are students undertaking learning tasks. While technical literature addresses QoE and educational literature discusses online learning, a gap exists describing the relationship of QoS and the quality of the learning experience. This work uses a mixed methods approach to address the research question: What dimensions of QoE of online learning can be affected by QoS? To answers this question, two groups of students were exposed to changing QoS conditions while they were undertaking an online learning activity using remote access technology. Both technical performance parameters, as well as, the impressions where recorded. Subsequently, a focus group was held to get a better understanding of the students' perception of the relationship between QoS, QoE and online learning tools. It is concluded that QoS factors only have an intermediate impact on the quality of the learning experience of the students. Factors such as course design and pedagogy largely determine the quality of online learning.

Quality of experience (QoE) is the perceptual quality of service (QoS) from the users' perspective. For video service, the relationship between QoE and QoS (such as coding parameters and network statistics) is complicated because users' perceptual video quality is subjective and diversified in different environments. Traditionally, QoE is obtained from subjective test, where human viewers evaluate the quality of tested videos under a laboratory environment. To avoid high cost and offline nature of such tests, objective quality models are developed to predict QoE based on objective QoS parameters, but it is still an indirect way to estimate QoE. With the rising popularity of video streaming over the Internet, data-driven QoE analysis models have newly emerged due to availability of large-scale data. In this paper, we give a comprehensive survey of the evolution of video quality assessment methods, analyzing their characteristics, advantages, and drawbacks. We also introduce QoE-based video applications and, finally, identify the future research directions of QoE.

Video quality evaluation needs a combined approach that includes subjective and objective metrics, testing, and monitoring of the network. This paper deals with the novel approach of mapping quality of service (QoS) to quality of experience (QoE) using QoE metrics to determine user satisfaction limits, and applying QoS tools to provide the minimum QoE expected by users. Our aim was to connect objective estimations of video quality with the subjective estimations. A comprehensive tool for the estimation of the subjective evaluation is proposed. This new idea is based on the evaluation and marking of video sequences using the sentinel flag derived from spatial information (SI) and temporal information (TI) in individual video frames. The authors of this paper created a video database for quality evaluation, and derived SI and TI from each video sequence for classifying the scenes. Video scenes from the database were evaluated by objective and subjective assessment. Based on the results, a new model for prediction of subjective quality is defined and presented in this paper. This quality is predicted using an artificial neural network based on the objective evaluation and the type of video sequences defined by qualitative parameters such as resolution, compression standard, and bitstream. Furthermore, the authors created an optimum mapping function to define the threshold for the variable bitrate setting based on the flag in the video, determining the type of scene in the proposed model. This function allows one to allocate a bitrate dynamically for a particular segment of the scene and maintains the desired quality. Our proposed model can help video service providers with the increasing the comfort of the end users. The variable bitstream ensures consistent video quality and customer satisfaction, while network resources are used effectively. The proposed model can also predict the appropriate bitrate based on the required quality of video sequences, defined using either objective or subjective assessment.

In video streaming service, the user's Quality of Experience (QoE) is not only related to video signal quality received at consumer's devices, the users’ subjectivity must also be considered. In this context, a video quality assessment method that takes into account the user's preference for video content is proposed in this research. In order to perform this task, the users' profiles that include their preferences were stored in a video server. Then, subjective tests of video quality assessment were conducted, in which evaluators had different video content preferences. Results show that the evaluators' QoE is highly correlated with the user's preference for video content type. Based on these experimental results, a function named Preference Factor (PF) is defined and used to adjust the quality index values obtained by an objective video quality metric running in the end user's device. The PF function also depends on video content type and quality index score. Using the PF function, the enhanced Video streaming Quality Metric (e-VsQM) is proposed and the results of its performance evaluation demonstrate that PF improves an objective video quality metric. Furthermore, e-VsQM has low complexity and can be utilized in different video services. Thus, an application scenario is presented, in which the proposed video quality metric is implemented.

A model that can predict end user satisfaction or QoE (Quality of Experience) directly from the network QoS (Quality of Service) is still illusive in the field of image processing. This motivates the derivation of a meaningful QoS to QoE mapping function to allow one to be predicted in the absence of the other. This paper presents an affine fuzzy logic based model that can estimate the visual perceptual quality for different video content types using a combination of network level and application level QoS parameters. Video contents are classified based on their spatio-temporal feature extraction. The video QoE is predicted in terms of the Mean Opinion Score (MOS). From the results it is clear that the QoE is video content dependent. Also, the network level parameters have more impact on video quality than the application level parameters. Results show that the Fuzzy logic-based model provides high prediction accuracy. The performance of the model was evaluated using a public dataset with good prediction accuracy (~ 95%). The developed model has use in control methods for streaming standard encoded video.

Recently, the evaluation on quality of the air interface in a wireless network has shifted from emphasis on QoS (quality of service) only to incorporation of QoE (quality of experience). However, it seems that QoE metrics have not been related to the surroundings of the served users so far, which may have a great influence on users' expected QoE. For instance, with a high level of QoS, a user's expectation of QoE could still be terrible because of the serious interference from the surroundings such as noise, ambient light or shaking of terminals due to mobility. From another perspective, it means that in such a case a high level of QoS may be helpless to enhance users' QoE and the requirement could be relaxed accordingly. In this paper, an approach called surrounding-adaptive QoE adjustment (SAQA) is proposed to optimize the capacity and the quality of air interface with voice service as an example. The basic idea of the approach is that user equipment (UE) detects the surroundings interference which may impact users' QoE and then adjusts QoS according to the detected results: a lower level of QoS is chosen in a bad environment to enhance the capacity, while a higher level of QoS is selected to guarantee the users' QoE in a better circumstance. Simulation results show that this method can remarkably enhance the voice capacity of the communication systems as well as guarantee users' QoE.