The increasing usage of numerous mobile applications can cause impairments in cellular network performance. Such impairments result in performance degradation that can reduce the satisfaction level of subscribers. Consequently, subscribers may switch between different network operators to get good user experience. Thus the success of any network operator will primarily depend on the ability to ensure quality of experience (QoE), where QoE is a measure of the subscriber’s satisfaction level and is closely related to the performance of networks. Our work aims to identify the key performance indicators (KPI)which in turn can comprehensively model the QoE. Since the popularity of web browsing and video streaming applications continues to increase rapidly, analyzing the KPI of such applications will help to identify the parameters which degrade network performance the most. The analyzed KPIs are tested with different user equipments and different network load. This thesis work also includes tuning the Radio Network Controller (RNC) parameters to analyze the variation in user experience. Important performance metrics of webbrowsing and video streaming applications have been considered to measure the QoE. Atest environment for QoE estimation was developed using real Radio Network Controller(RNC) and simulatable models of the Core Network(CN) and User Equipments (UEs). Simulations with this test set up and subsequent analyses help to identify some of the RNC parameters which influence the QoE. Furthermore, simulatable models of widely used UEssuch as iPhone 6 and iPhone 3 were included in the test environment to assess their relative performance for web browsing and video streaming applications. Our simulation results confirm the superior performance of iPhone 6 which reinforces the reliability of our testbed. Finally, the simulations also helped to illustrate the degradation in QoE caused by the increase in RNC load.

The recent years have observed a tremendous shift from the technology-centric assessment to the user-centric assessment of network services. Consequently, measurement and modelling of Quality of Experience (QoE) attracted many contributions from researchers and practitioners. Generally, QoE is assessed via active and passive measurements. While the former usually allows QoE assessment on the test traffic, the latter opens avenues for continuous QoE assessment on the real traffic generated by the users. This thesis contributes towards passive assessment of QoE. This thesis document begins with a background on the fundamentals of network management and objective QoE assessment. It extends the discussion further to the QoE-centric monitoring and management of network, complimented by the details about QoE estimator agent developed within the Celtic project QuEEN (Quality of Experience Estimators in Network). The discussion on findings starts with results from subjective tests to understand the relationship between waiting times and user subjective feedback over time. These results strengthen the understanding of timescales on which users react, as well as, the effect of user memory on QoE. The findings show that QoE drops significantly when the user faces recurring waiting times of 0.5 s to 4 s durations in case of video streaming and web browsing services. With recurring network disturbances within every 8 s – 16 s time intervals, the user tolerance to waiting times decreases constantly, showing the sign of user memory of recent disturbances. Subsequently, this document introduces and evaluates a passive wavelet-based QoE monitoring method. The method detects timescales on which transient outages occur frequently. A study presents results from qualitative measurements, showing the ability of wavelet to differentiate on-fly between “Good” and “Bad” traffic streams. In sequel, a quantitative study systematically evaluates wavelet-based metrics. Subsequently, the subjective evaluation and wavelet analysis of 5 – 6 minutes long video streaming sessions on mobile networks show that wavelet-based metrics is indeed useful for passive monitoring of QoE issues. Finally, this thesis investigates a method for passive monitoring of user reactions to degrading network performance. The method is based on the TCP termination flags. With a systematic evaluation in a test environment, the results characterise termination of data transfers in case of different user actions in the web browser.

This thesis assesses the impact of Wi-Fi radio frequency interference (RFI) on mobile application quality of experience (QoE). Wi-Fi is a wireless radio network for transferring data between two end points and is based on the IEEE 802.11 standards and operates in the unlicensed 2.4 GHz and 5 GHz radio frequency bands. This thesis explores the QoE of mobile applications when considering the impact of RFI caused by Wi-Fi access points (WAPs) within a campus Wi-Fi network. The research was conducted to assess the effect of RFI on mobile application network performance metrics. This is evaluated by collecting broadcasted WAPs within a campus network, assessing the experienced RFI, and evaluating the mobile application QoE at specific locations to assess the impact of the experienced interference. / Graduate

In this thesis various aspects of Quality of Experience (QoE) research are examined. The work is divided into three major blocks: QoE Assessment, QoE Monitoring, and VNF Performance Evaluation. First, prominent cloud applications such as Google Docs and a cloud-based photo album are explored. The QoE is characterized and the influence of packet loss and delay is studied. Afterwards, objective QoE monitoring for HTTP Adaptive Video Streaming (HAS) in the cloud is investigated. Additionally, by using a Virtual Network Function (VNF) for QoE monitoring in the cloud, the feasibility of an interworking of Network Function Virtualization (NFV) and cloud paradigm is evaluated. To this end, a VNF that exploits deep packet inspection technique was used to parse the video traffic. An algorithm is then designed accordingly to estimate video quality and QoE based on network and application layer parameters. To assess the accuracy of the estimation, the VNF is measured in different scenarios under different network QoS and the virtual environment of the cloud architecture. The insights show that the different geographical deployments of the VNF influence the accuracy of the video quality and QoE estimation. Various Service Function Chain (SFC) placement algorithms have been proposed and compared in the context of edge cloud networks. On the one hand, this research is aimed at cloud service providers by providing methods for evaluating QoE for cloud applications. On the other hand, network operators can learn the pitfalls and disadvantages of using the NFV paradigm for such a QoE monitoring mechanism. / In dieser Arbeit werden verschiedene Aspekte von Quality of Experience (QoE) und QoE-Monitoring untersucht. Die Arbeit teilt sich in drei große Blöcke auf: QoE Assessment, QoE Monitoring und Leistungsuntersuchung einer VNF. Zunächst werden prominente Cloud-Anwendungen wie Google Docs und ein Cloud-basiertes Photoalbum untersucht. Die QoE wird charakterisiert und es wird der Einfluss von Paketverlust und Delay studiert. Danach wird das objektive QoE-Monitoring für HTTP Adaptive Video Streaming (HAS) in der Cloud untersucht. Durch die Verwendung einer virtuellen Netzwerkfunktion (Virtual Network Function, VNF) für die QoE-Überwachung in der Cloud wurde außerdem die Durchführbarkeit eines Zusammenwirkens von Netzwerkfunktionsvirtualisierung (NFV) und Cloud-Paradigma bewertet. Zu diesem Zweck wurde der VNF, die die Deep-Packet-Inspection-Technik benutzt, zum Parsen des Videoverkehrs verwendet. Im Anschluss wurde ein Algorithmus entworfen, um die Videoqualität und die QoE basierend auf Netzwerk- und Anwendungsschichtparametern zu schätzen. Um die Genauigkeit der Schätzung zu bewerten, wurde die VNF in verschiedenen Szenarien unter verschiedener Netzwerk-QoS und der virtuellen Umgebung der Cloud-Architektur gemessen. Die Erkenntnisse zeigen, dass die unterschiedlichen geografischen Implementierungen der VNF die Genauigkeit der Schätzung der Videoqualität und QoE beeinflussen. Es wurden verschiedene Platzierungsalgorithmen der Service Function Chain (SFC) vorgeschlagen und im Kontext von Edge-Cloud-Netzwerken verglichen. Diese Forschungsarbeit zielt zum einen auf Cloud-Service-Provider ab, indem ihnen Methoden zur Bewertung der QoE für Cloud-Anwendungen zur Verfügung gestellt werden. Auf der anderen Seite können die Netzwerkbetreiber die Fallstricke und Nachteile der Anwendung des NFV-Paradigmas für einen solchen QoE-Überwachungsmechanismus erlernen.

This thesis is focused on the subjective measurement of the quality of experience on streaming video through a mobile application. After wide analysis of possibilities how to realize subjective measurement of quality of experience, the mobile and web application were created. These tools enable to obtain user ratings of streaming videos and then evaluate them. The user ratings are stored to the central database via secured REST API.

The usage of smartphones has been increasing with surprising speed. These smartphones are popular for delivery of video content. The main drawbacks of these smartphones are battery life and video freezing. Despite, while streaming a video it consumes large of amount of power affecting QoE. So, in this case we considered streaming a video from server to mobile client involving ONOFF characteristics. While streaming, there exists some transition delay while switching the power states and the effect of these transition delays might affect instantaneous power consumption of the smartphone. Henceforth, this thesis aims to determine the effect on instantaneous power consumption from distributed state durations and transitions in exponential fluid flow model, for a streamed video. Power measurements along with ON and OFF times were measured with the help of a benchmark tool, Monsoon Power Monitor tool. VLQoE tool, a video streaming tool was used to present a two state model based on the inter-picture time, for the HTTP-based video streaming. Experiments were executed in a closed enclosure setup using a black-box to avoid external obstacles that might possibly affect the power consumption metrics. Considering these measurements, the effect on instantaneous power consumption stemming from the exponentially distributed state durations and transitions in the corresponding fluid flow model can be determined and modelled.

Customer churn has always been a problem to be addressed by the telecommunication service providers. So far, work done in this regard was based on analyzing historical data of the customers by using different data mining techniques. Investigations based on individual user behavior with a motive of churn prediction are expected to give an idea about the user’s point view towards churn. Data volumes/data usage of the users is seen as parameter to assess the satisfaction of the users with the service. The subjective and objective behavior of the mobile phone users has been captured by collecting data about the data volumes/data usage for both Wi-Fi and mobile services along with their ratings of Quality of Experience (QoE).   The Experience Sampling Method has been deployed to collect the user data. Android tool was used to collect weekly data volumes of the users. A questionnaire was prepared with questions regarding quality, annoyance and churn risk of the users. The questionnaire was used to collect the weekly opinions of the users on the service. A total of 22 users participated in the study, of which 3 persons churned to other service provider during the study. The data collected in the study was analyzed using averages, correlations and decision trees. Comparisons were made between Wi-Fi and mobile services, churners and non-churners/active users. A 2-fold churn prediction model was proposed based on conclusions of the study.

Customer churn is always a grievous issue for the Telecom industry as customers do not hesitate to leave if they don’t find what they are looking for. They certainly want competitive pricing, value for money and above all, high quality service. Customer churning is directly related to customer satisfaction. It’s a known fact that the cost of customer acquisition is far greater than cost of customer retention, that makes retention a crucial business prototype. There is no standard model which addresses the churning issues of global telecom service providers accurately. BigData analytics with Machine Learning were found to be an efficient way for identifying churn. This thesis aims to predict customer churn using Big Data analytics, namely a J48 decision tree on a Java based benchmark tool, WEKA. Three different datasets from various sources were considered; first includes Telecom operator’s six month aggregate active and churned users’ data usage volumes, second includes globally surveyed data and third dataset comprises of individual weekly data usage analysis of 22 android customers along with their average quality, annoyance and churn scores by accompanying theses. Statistical analyses and J48 Decision trees were drawn for three different datasets. From the statistics of normalized volumes, autocorrelations were small owing to reliable confidence intervals, but confidence intervals were overlapping and close by, therefore no much significance could be noticed, henceforth no strong trends could be observed. From decision tree analytics, decision trees with 52%, 70% and 95% accuracies were achieved for three different data sources respectively.      Data preprocessing, data normalization and feature selection have shown to be prominently influential. Monthly data volumes have not shown much decision power. Average Quality, Churn Risk and to some extent, Annoyance scores may point out a probable churner. Weekly data volumes with customer’s recent history and necessary attributes like age, gender, tenure, bill, contract, data plan, etc., are pivotal for churn prediction.

With the increase in demand for video streaming services on the hand held mobile terminals with limited battery life, it is important to maintain the user Quality of Experience (QoE) while taking the resource consumption into consideration. Hence, the goal is to offer as good quality as feasible, avoiding as much user-annoyance as possible. Hence, it is essential to deliver the video, avoiding any uncontrollable quality distortions. This can be possible when an optimal (or desirable) throughput value is chosen such that exceeding the particular threshold results in entering a region of unstable QoE, which is not feasible. Hence, the concept of QoE-aware sustainable throughput is introduced as the maximal value of the desirable throughput that avoids disturbances in the Quality of Experience (QoE) due to delivery issues, or keeps them at an acceptable minimum. The thesis aims at measuring the sustainable throughput values when video streams of different resolutions are streamed from the server to a mobile client over wireless links, in the presence of network disturbances packet loss and delay. The video streams are collected at the client side for quality assessment and the maximal throughput at which the QoE problems can still be kept at a desired level is determined. Scatter plots were generated for the individual opinion scores and their corresponding throughput values for the disturbance case and regression analysis is performed to find the best fit for the observed data. Logarithmic, exponential, linear and power regressions were considered in this thesis. The R-squared values are calculated for each regression model and the model with R-squared value closest to 1 is determined to be the best fit. Power regression model and logarithmic model have the R-squared values closest to 1.  Better quality ratings have been observed for the low resolution videos in the presence of packet loss and delay for the considered test cases. It can be observed that the QoE disturbances can be kept at a desirable level for the low resolution videos and from the test cases considered for the investigation, 360px video is more resilient in case of high delay and packet loss values and has better opinion score values. Hence, it can be observed that the throughput is sustainable at this threshold.

The constant growth in network communications technologies and the emergence of Service Specific Overlay Networks (SSONs), coupled with the rapid development of multimedia applications make the management of such technologies a major challenge. This thesis investigates the SSONs management problem and proposes an autonomic architecture, a self-organizing and self-adapting algorithm, and a utility function for monitoring the Quality of Experience (QoE) of IPTV streams in SSONs. First, we examine the different issues stemming from the autonomic management of SSONs and identify the limitations of existing approaches. We then propose an architecture to ease the management of SSONs by incorporating autonomic computing principles to make SSONs acquire self-management capabilities. The proposed architecture introduces autonomic control loops that continuously monitor network components and analyze the gathered data. An Autonomic System (AS) is comprised of one or more Autonomic Managers (AM) which take control of managing other elements in the network. The proposed architecture highlights the different components of an AM and identifies its purpose. The distributed nature of the proposed architecture avoids limitations of centralized management solutions. We then propose a scheme to allow AMs to emerge among the set of nodes in the network as the most powerful ones in terms of different factors, including processing capabilities and stability. Using a self-organizing and self-adapting distributed protocol, each node in the overlay selects an appropriate AM to report to so that sensed data is delivered error-free, and in a timely manner, while the load is distributed over the AMs. Finally, we propose a utility function to monitor the quality of IPTV streams by predicting QoE based on statistical Quality of Service (QoS) information. The proposed function is simple and does not require high processing power. It allows the QoE of IPTV users to be monitored in real-time by the AMs, so that quality degradations are accurately identified and adaptation mechanisms are triggered at the right moment to correct issues causing degradations. Theoretical analysis and simulations studies are presented to demonstrate the performance of the proposed schemes.