Nowadays, employees have to work with applications, technical services, and systems every day for hours. Hence, performance degradation of such systems might be perceived negatively by the employees, increase frustration, and might also have a negative effect on their productivity. The assessment of the application's performance in order to provide a smooth operation of the application is part of the application management. Within this process it is not sufficient to assess the system performance solely on technical performance parameters, e.g., response or loading times. These values have to be set into relation to the perceived performance quality on the user's side - the quality of experience (QoE). This dissertation focuses on the monitoring and estimation of the QoE of enterprise applications. As building models to estimate the QoE requires quality ratings from the users as ground truth, one part of this work addresses methods to collect such ratings. Besides the evaluation of approaches to improve the quality of results of tasks and studies completed on crowdsourcing platforms, a general concept for monitoring and estimating QoE in enterprise environments is presented. Here, relevant design dimension of subjective studies are identified and their impact of the QoE is evaluated and discussed. By considering the findings, a methodology for collecting quality ratings from employees during their regular work is developed. The method is realized by implementing a tool to conduct short surveys and deployed in a cooperating company. As a foundation for learning QoE estimation models, this work investigates the relationship between user-provided ratings and technical performance parameters. This analysis is based on a data set collected in a user study in a cooperating company during a time span of 1.5 years. Finally, two QoE estimation models are introduced and their performance is evaluated. / Heutzutage sind Geschäftsanwendungen und technische Systeme aus dem Arbeitsalltag vieler Menschen nicht mehr wegzudenken. Kommt es bei diesen zu Performanzproblemen, wie etwa Verzögerungen im Netzwerk oder Überlast im Datenzentrum, kann sich dies negativ auf die Effizienz und Produktivität der Mitarbeiter auswirken. Daher ist es wichtig aus Sicht der Betreiber die Performanz der Anwendungen und Systeme zu überwachen. Hierbei ist es allerdings nicht ausreichend die Qualität lediglich anhand von technischen Performanzparametern wie Antwortzeiten zu beurteilen. Stattdessen sollten diese Werte in Relation zu der von den Mitarbeitern wahrgenommenen Performanz oder Quality of Experience (QoE) gesetzt werden. Diese Dissertation beschäftigt sich mit dem Monitoring und der Abschätzung der QoE von Geschäftsanwendungen. Neben der Präsentation eines generellen Konzepts zum Monitoring und der Abschätzung der QoE im Geschäftsumfeld, befasst sich die Arbeit mit Aspekten der Erfassung von Qualitätsbewertungen durch die Nutzer. Dies umfasst einerseits die Evaluation von Ansätzen zur Verbesserung der Qualität von Aufgaben- und Studienergebnissen auf Crowdsourcing-Plattformen. Andererseits werden relevante Dimensionen des Designs von Studien zur Untersuchung der QoE von Geschäftsanwendungen aufgezeigt und deren Einfluss auf die QoE diskutiert und evaluiert. Letztendlich wird eine Methodik zur Erfassung von Qualitätsbewertungen durch Mitarbeiter während ihrer regulären Arbeit vorgestellt, welche implementiert und in einem kooperierenden Unternehmen ausgerollt wurde. Als Grundlage der Entwicklung eines QoE Abschätzungsmodells, untersucht diese Arbeit den Zusammenhang zwischen Bewertungen durch die Nutzer und technischen Performanzparametern. Die Untersuchungen erfolgen auf einem Datensatz, welcher in einer Studie über 1.5 Jahre in einem kooperierenden Unternehmen gesammelt wurde. Des Weiteren werden zwei Methoden zur Abschätzung der QoE präsentiert und deren Performanz evaluiert.

In the past two decades, there has been a trend to move from traditional television to Internet-based video services. With video streaming becoming one of the most popular applications in the Internet and the current state of the art in media consumption, quality expectations of consumers are increasing. Low quality videos are no longer considered acceptable in contrast to some years ago due to the increased sizes and resolution of devices. If the high expectations of the users are not met and a video is delivered in poor quality, they often abandon the service. Therefore, Internet Service Providers (ISPs) and video service providers are facing the challenge of providing seamless multimedia delivery in high quality. Currently, during peak hours, video streaming causes almost 58\% of the downstream traffic on the Internet. With higher mobile bandwidth, mobile video streaming has also become commonplace. According to the 2019 Cisco Visual Networking Index, in 2022 79% of mobile traffic will be video traffic and, according to Ericsson, by 2025 video is forecasted to make up 76% of total Internet traffic. Ericsson further predicts that in 2024 over 1.4 billion devices will be subscribed to 5G, which will offer a downlink data rate of 100 Mbit/s in dense urban environments. One of the most important goals of ISPs and video service providers is for their users to have a high Quality of Experience (QoE). The QoE describes the degree of delight or annoyance a user experiences when using a service or application. In video streaming the QoE depends on how seamless a video is played and whether there are stalling events or quality degradations. These characteristics of a transmitted video are described as the application layer Quality of Service (QoS). In general, the QoS is defined as "the totality of characteristics of a telecommunications service that bear on its ability to satisfy stated and implied needs of the user of the service" by the ITU. The network layer QoS describes the performance of the network and is decisive for the application layer QoS. In Internet video, typically a buffer is used to store downloaded video segments to compensate for network fluctuations. If the buffer runs empty, stalling occurs. If the available bandwidth decreases temporarily, the video can still be played out from the buffer without interruption. There are different policies and parameters that determine how large the buffer is, at what buffer level to start the video, and at what buffer level to resume playout after stalling. These have to be finely tuned to achieve the highest QoE for the user. If the bandwidth decreases for a longer time period, a limited buffer will deplete and stalling can not be avoided. An important research question is how to configure the buffer optimally for different users and situations. In this work, we tackle this question using analytic models and measurement studies. With HTTP Adaptive Streaming (HAS), the video players have the capability to adapt the video bit rate at the client side according to the available network capacity. This way the depletion of the video buffer and thus stalling can be avoided. In HAS, the quality in which the video is played and the number of quality switches also has an impact on the QoE. Thus, an important problem is the adaptation of video streaming so that these parameters are optimized. In a shared WiFi multiple video users share a single bottleneck link and compete for bandwidth. In such a scenario, it is important that resources are allocated to users in a way that all can have a similar QoE. In this work, we therefore investigate the possible fairness gain when moving from network fairness towards application-layer QoS fairness. In mobile scenarios, the energy and data consumption of the user device are limited resources and they must be managed besides the QoE. Therefore, it is also necessary, to investigate solutions, that conserve these resources in mobile devices. But how can resources be conserved without sacrificing application layer QoS? As an example for such a solution, this work presents a new probabilistic adaptation algorithm that uses abandonment statistics for ts decision making, aiming at minimizing the resource consumption while maintaining high QoS. With current protocol developments such as 5G, bandwidths are increasing, latencies are decreasing and networks are becoming more stable, leading to higher QoS. This allows for new real time data intensive applications such as cloud gaming, virtual reality and augmented reality applications to become feasible on mobile devices which pose completely new research questions. The high energy consumption of such applications still remains an issue as the energy capacity of devices is currently not increasing as quickly as the available data rates. In this work we compare the optimal performance of different strategies for adaptive 360-degree video streaming. / In den vergangenen zwei Jahrzehnten gab es einen starken Trend weg vom traditionellen Fernsehen hin zum Videostreaming über das Internet. Dabei macht Videostreaming zurzeit den größten Anteil des gesamten Internetverkehrs aus. Beim Herunterladen eines Internetvideos wird das Video vor dem Ausspielen in einem Puffer beim Client zwischengespeichert, um Netzfluktuationen zu kompensieren. Leert sich der Puffer, so muss das Video stoppen (Stalling), um Daten nachzuladen. Um dies zu verhindern, müssen Pufferstrategien und -Parameter optimal an Nutzerszenarien angepasst sein. Mit diesem Problem beschäftigen wir uns im ersten Kapitel dieser Arbeit unter Anwendung von Wartschlangenmodelle, numerische Simulationen und Messstudien. Zur Bewertung der Güte eines Videostreams nutzen wir ein Modell, das auf subjektiven Studien basiert. Mit HTTP Adaptive Streaming hat der Videoplayer die Fähigkeit, Videosegmente in einer an die Bandbreite angepasster Bitrate und somit auch angepasster Qualität anzufordern. Somit kann die Leerung des Puffers gebremst und Stalling verhindert werden. Allerdings hat neben Stalling auch die Videoqualität und die Anzahl der Qualitätswechsel Auswirkungen auf die Zufriedenheit der Zuschauer. Inwiefern diese Parameter optimiert werden können, untersuchen wir im zweiten Kapitel mit Hilfe von linearen und quadratischen Programmen sowie einem Warteschlangenmodell. Hierbei untersuchen wie auch die Fairness in Netzen mit mehreren Nutzern und 360-Grad Videos. Im dritten Kapitel untersuchen wir Möglichkeiten, Videostreaming ressourcenschonender zu gestalten. Hierzu untersuchen wir in einer Feldstudie die Möglichkeit Caches an WiFi-Hotspots einzusetzen und somit redundanten Verkehr zu reduzieren. Wir untersuchen das Verhalten von mobilen Videonutzern, indem wir eine Nutzerstudie auswerten. Außerdem stellen wir einen neuen Adaptionsalgorithmus vor, der abhängig vom Nutzerverhalten den Datenverbrauch und Stromverbrauch des Videostreams reduziert.

Yes / IET Editorial: In the future communications will be pervasive in nature, allowing users access at the “touch of button” to attain any service, at any time, on any device. The future device design process requires both a reconfigurable RF front end and back end with high tuning speed, energy efficiency, excellent linearity and intelligence to maximise the “greenness” of the network. But energy efficiency and excellent linearity are the main topics that are driving the designs of future transceivers, including their efforts to minimise network contributions to climate changes such as the effect of CO2 emissions: the minimisation of these is a requirement for information and communication technology (ICT) as much as for other technologies. Recently, information and communication technologies were shown to account for 3% of global power consumption and 2% of global CO2 emissions, and hence far from insignificant. The approach towards energy conservation and CO2 reduction in future communications will require a gret deal of effort which should be targeted both at the design of energy efficient, low-complexity physical, MAC and network layers, while maintaining the required Quality of Service (QoS). There is also a need, in infrastructures, networks and user terminals, to take a more holistic approach to improving or achieving green communications, from radio operation, through functionality, up to implementation. The increasing demand for data and voice services is not the only cause for concern since energy management and conservation are now at the forefront of the political agenda. The vision of Europe 2020 is to become a smart, sustainable and inclusive economy, and as part of these priorities the EU have set forth the 20:20:20 targets, whereby greenhouse gas emissions and energy consumption should be reduced by 20% while energy from renewables should be increased by 20%.

Předložená dizertační práce se zabývá moderními algoritmy pro kódovaní videosekvencí, zejména algoritmem High Efficiency Video Coding, a jeho použítím v prostředí online streamování. Vzhledem k tomu, že chování koncových diváků směřuje ke sledování video obsahu kdykoli a kdekoli, způsob, jakým je obsah doručen k divákovi, se stává stejně důležitým, jakým je samotné kódování. V této práci se zaměřujeme na užití HEVC ve službách založených na HTTP adaptivním streamování, zejména ve službách využívajích DASH. Dále se zabýváme dalšími aspekty, které mají vliv na kvalitu zážitku (Quality of Experience) tak, jak jej vnímá koncový uživatel. Takovými jsou na příklad přítomnost reklamy či další systémové parametry. Abychom mohli sbírat názory uživatelů, pro naše experimenty často používáme crowdsourcing. Z tohoto důvodu je část této práce věnována samotnému crowdsourcingu a tomu, jak jej lze využít pro hodnocení kvality videa.

We report on our experiences from two user studies (lab experiments) with nearly 300 participants for QoE evaluation using real mobile services and devices in our WiFi network emulation testbed. We briefly introduce our principles for integrating real services in these studies: how we selected relevant services, how we investigated their testability and how we tested them with high efficiency.

We report on our experiences from two user studies (lab experiments) with nearly 300 participants for QoE evaluation using real mobile services and devices in our WiFi network emulation testbed. We briefly introduce our principles for integrating real services in these studies: how we selected relevant services, how we investigated their testability and how we tested them with high efficiency.

In der theoretischen Auseinandersetzung mit mobilen Internet-Diensten sind Quality of Service (QoS) und Quality of Experience (QoE) als hochkomplexe und verbundene Konzepte zu erkennen. QoS umfasst dabei die technische Sicht auf das Telekommunikationsnetz, charakterisiert durch leistungsrelevante Parameterwerte (z. B. Durchsatz und Latenz). QoE hingegen bezieht sich auf die Bewertung des Nutzererlebnisses (z. B. Zufriedenheit und Akzeptanz). Zur gemeinsamen Erklärung bedarf es einer multi- bzw. interdisziplinären Betrachtung zwischen Ingenieurs- und Humanwissenschaften, da neben der Technik auch der Mensch als Nutzer in den QoS-QoE-Zusammenhang involviert ist. Ein mehrschichtiges Modell erfasst die relevanten Einflussfaktoren und internen Zusammenhänge zwischen QoS und QoE sowohl aus Netz- als auch Nutzersicht. Zur Quantifizierung des Zusammenhangs konkreter Werte in einer empirischen QoE-Evaluation wurde ein umfangreiches psychophysikalisches Laborexperiment konzipiert. Das dafür entwickelte Netzemulations-Testbed erlaubt mobiltypische Netz- und Nutzungssituationen gezielt in einem Testparcours zusammenzubringen. Die formulierten Prinzipien zur Testrelevanz, -eignung und -effizienz berücksichtigen hierbei die Besonderheiten des Testaufbaus und -designs mit echten Endgeräten und Diensten. Die Ergebnisse von über 200 Probanden bestätigen die vorhergesagten QoS-QoE-Charakteristiken der sechs untersuchten Dienste als kontinuierlich-elastisch bzw. sprunghaft-fest. Dienstspezifisch lässt sich jeweils von einem angestrebten Grad der Nutzerzufriedenheit auf die notwendigen Werte der QoS-Netzparameter schließen, woraus sich ein QoS-QoE-Zufriedenheitskorridor zwischen einem unteren und oberen Schwellwert ergibt. Teilweise sind dabei QoS-unabhängige Faktoren, z. B. die Art der Präsentation der Stimuli in der App auf dem Endgerät, als ebenso relevant zu erkennen wie die QoS-Netzparameter selbst. / The thesis is centered on the relationship of Quality of Service (QoS) and Quality of Experience (QoE) for mobile Internet services. While QoS covers the technical view on the telecommunications network characterized by performance-related parameter values (e.g. throughput and latency), QoE refers to the assessment of the user experience (e.g. satisfaction and acceptability) in the use of the services. In the thesis QoS and QoE are revealed as highly complex and related concepts in theoretical contemplation. Integrating both concepts requires a multidisciplinary or interdisciplinary approach between engineering and human sciences to consider both - technological aspects of the network as well the human user. The designed multilayered model appropriately integrates the technical network view as well as the user's perspective by considering all relevant factors of influence and all internal relationships between QoS and QoE. The conducted extensive psychophysical laboratory experiment with real users, devices and services quantifies the relationship between specific QoS values and specific QoE values. A testbed developed for network emulation allows combining typical mobile network situations with typical usage situations in a controlled and focused manner. The three elaborated principles to test for relevance, suitability and efficiency take into account the special features of the test setup and test design. Test results gained from more than 200 volunteers confirm the predicted QoS-QoE-characteristics of the six tested mobile services to be either elastic or non-elastic. It is possible to conclude from the desired degree of user satisfaction on the necessary values of the QoS network parameters, which results in a QoS-QoE-corridor between lower and upper threshold values. Findings prove that QoS-independent factors, e.g. the type of presentation of the stimuli in the app on the user’s device, can be as relevant for QoE as the evaluated QoS network parameters themselves.

As there is a rapid growth in the field of wireless communications, the demand for various multimedia services is also increasing. The data that is being transmitted suffers from distortions through source encoding and transmission over errorprone channels. Due to these errors, the quality of the content is degraded. There is a need for service providers to provide certain Quality of Experience (QoE) to the end user. Several methods are being developed by network providers for better QoE.The human tendency mainly focuses on distortions in the Region of Interest(ROI) which are perceived to be more annoying compared to the Background(BG). With this as a base, the main aim of this thesis is to get an accurate prediction quality metric to measure the quality of the image over ROI and the BG independently. Reduced Reference Image Quality Assessment (RRIQA), a reduced reference image quality assessment metric, is chosen for this purpose. In this method, only partial information about the reference image is available to assess the quality. The quality metric is measured independently over ROI and BG. Finally the metric estimated over ROI and BG are pooled together to get aROI aware metric to predict the Mean Opinion Score (MOS) of the image.In this thesis, an ROI aware quality metric is used to measure the quality of distorted images that are generated using a wireless channel. The MOS of distorted images are obtained. Finally, the obtained MOS are validated with the MOS obtained from a database [1].It is observed that the proposed image quality assessment method provides better results compared to the traditional approach. It also gives a better performance over a wide variety of distortions. The obtained results show that the impairments in ROI are perceived to be more annoying when compared to the BG.

In spite of technological advances in wireless systems, transmitted data suffers from impairments through both lossy source coding and transmission overerror prone channels. Due to these errors, the quality of multimedia content is degraded. The major challenge for service providers in this scenario is to measure the perceptual impact of distortions to provide certain Quality of Experience(QoE) to the end user. The general tendency of the Human Visual System (HVS) suggests that the artifacts in the Region-of-Interest (ROI) are perceived to be more annoying compared to the artifacts in Background (BG). With this assumption, the thesis aims to measure the quality of image over ROI and BG independently. Visual Information Fidelity (VIF), a full-reference image quality assessment is chosen for this purpose. Finally, the metric measured over ROI and BG are pooled to get a ROI aware metric. The ROI aware metric is used to predict the Mean Opinion Score (MOS) of an image. In this study, an ROI aware quality metric is used to measure the quality of a set of distorted images generated using a wireless channel. Eventually, MOS of the distorted images is estimated. Lastly, the predicted MOS is validated with the MOS obtained from subjective tests. Testing the proposed image quality assessment approach shows an improved prediction performance of ROI aware quality metric over traditional image quality metrics. It is also observed that the above approach provides a consistent improvement over a wide variety of distortions. After extensive research, the obtained results suggest that the impairments in the ROI are perceived to be more annoying than that of the BG.

The video streaming applications contribute to a major share of the Internet traffic. Consequently, monitoring and management of video streaming quality has gained a significant importance in the recent years. The disturbances in the video, such as, amount of buffering and bitrate adaptations affect user Quality of Experience (QoE). Network operators usually monitor such events from network traffic with the help of Deep Packet Inspection (DPI). However, it is becoming difficult to monitor such events due to the traffic encryption. To address this challenge, this thesis work makes two key contributions. First, it presents a test-bed, which performs automated video streaming tests under controlled time-varying network conditions and measures performance at network and application level. Second, it develops and evaluates machine learning models for the detection of video buffering and bitrate adaptation events, which rely on the information extracted from packets headers. The findings of this work suggest that buffering and bitrate adaptation events within 60 second intervals can be detected using Random Forest model with an accuracy of about 70%. Moreover, the results show that the features based on time-varying patterns of downlink throughput and packet inter-arrival times play a distinctive role in the detection of such events.