The idea of a Future Internet platform is to deliver reusable and common functionalities to facilitate making wide ranges of software products and services.  The Future Internet platform, introduced by the Future Internet Public Private Partnership (FI-PPP) project, makes the common functionalities available through so-called Enablers to be instantly integrated into software products and services with less cost and complexity rather than a development from scratch. Quality assessment of software products and services and gaining insights into whether the quality fulfills users’ expectations within the platform are challenging. The challenges are due to the propagation of quality in the heterogeneous composite software that uses Enablers and infrastructure developed by third parties. The practical problem is how to assess the quality of such composite software as well as the impacts of the quality on users’ Quality of Experience (QoE). The research objective is to study an analytics-driven Quality-Impact approach identifying how software quality analytics together with their impact on QoE of users can be used for the assessment of software products and services in a Future Internet platform. The research was conducted with one systematic mapping study, two solution proposals, and one empirical study. The systematic mapping study is contributed to produce a map overviewing important analytics for managing a software ecosystem. The thesis also proposes a solution to introduce a holistic software-human analytics approach in a Future Internet platform. As the core of the solution, it proposes a Quality-Impact inquiry approach exemplified with a real practice. In the early validation of the proposals, a mixed qualitative-quantitative empirical research is conducted with the aim of designing a tool for the inquiry of user feedback. This research studies the effect of the instrumented feedback tool on QoE of a software product. The findings of the licentiate thesis show that satisfaction, performance, and freedom from risks analytics are important groups of analytics for assessing software products and services.  The proposed holistic solution takes up the results by describing how to measure the analytics and how to assess them practically using a composition model during the lifecycle of products and services in a Future Internet platform. As the core of the holistic approach, the Quality-Impact assessment approach could elicit relationships between software quality and impacts of the quality on stakeholders. Moreover, the early validation of the Quality-Impact approach parameterized suitable characteristics of a feedback tool. We found that disturbing feedback tools have negligible impacts on the perceived QoE of software products. The Quality-Impact approach is helpful to acquire insight into the success of software products and services contributing to the health and sustainability of the platform. This approach was adopted as a part of the validation of FI-PPP project. Future works will address the validation of the Quality-Impact approach in the FI-PPP or other real practices.

With the development of three-dimensional (3D) technologies, the demand for high-quality 3D content, 3D visualization, and flexible and natural interactions are increasing. As a result, semi-transparent Augmented-Reality (AR) systems are emerging and evolving rapidly. Since there are currently no well-recognized models to evaluate the performance of these systems, we proposed a Quality-of-Experience (QoE) taxonomy for semi-transparent AR systems containing three levels of influential QoE parameters, through analyzing existing QoE models in other related areas and integrating the feedbacks received from our user study. We designed a user study to collect training and testing data for our QoE model, and built a Fuzzy-Inference-System (FIS) model to estimate the QoE evaluation and validate the proposed taxonomy. A case study was also conducted to further explore the relationships between QoE parameters and technical QoS parameters with functional components of Microsoft HoloLens AR system. In this work, we illustrate the experiments in detail and thoroughly explain the results obtained. We also present the conclusion and future work.

Alors que les services de streaming vidéo (Netflix +YouTube) représentent plus de 50% du trafic web des foyers américains sur leurs lignes fixes, l'expansion de la mobilité (4G LTE et LTEa) et l'arrivée des réseaux du futur (NGN) avec la distinction entre opérateur de réseaux et opérateurs de services, risquent de modifier le modèle économique des fournisseurs de service. Le modèle en Best-effort utilisé actuellement par les services de streaming risque de péricliter dû au coût important lié à ce type de fonctionnement. Il convient donc de réfléchir à un modèle optimisant les flux du service à envoyer en fonction du besoin réel des utilisateurs finaux. L'objectif principal de cette thèse est donc de proposer une solution permettant de prendre en compte la QoE au sein des services de streaming. Dans une première partie de cette thèse, nous mettons en avant le caractère très personnel de la qualité d'expérience lors d'une session de visionnage multimédia. Ceci est réalisé par des tests subjectifs sur une cinquantaine d'utilisateurs. Nous présentons ensuite une architecture permettant la prise en compte de la QoE au sein des protocoles de streaming HTTP adaptatifs (HTTP Live Streaming, DASH ...) afin de prendre des décisions d'adaptation du service non plus sur la bande passante disponible à un instant T, mais sur les attentes implicites de l'utilisateur et ceci d'une manière non intrusive. L‘implémentation d'une preuve du concept est illustrée à travers la création d'un projet open source (http://ipfhas.github.io/) / While streaming video services (Netflix + YouTube} represent more than 50% of US households consumed bandwidth, the growth of mobility (4G LTE + LTEa) and the arrival of 3 next generation networks (NGN) with the distinction between network operator and service operator may change the business model of service providers. The Best-Effort model currently used by streaming services will decrease due to its hight cost. It is therefore necessary to consider a model optimizing the service flow based on real users expectations. The main objective of this thesis is to propose a solution which take into account the QoE in streaming services. In the first art of this thesis, we highlight the very personal nature of the quality of experience during viewing multimedia session. This is achieved by subjective tests on fifty users. Then, we present an architecture for the QoE consideration in HTTP adaptive streaming protocols (HTTP Live Streaming, DASH ...) to make adaptation decisions based on implicit expectations of the user. A proof of concept is illustrated through an open source project (http://ipfhas.github.io/)

<p>To provide universal multimedia experience, multimedia streaming services need to transparently handle the variation and heterogeneity in operating environment. From the standpoint of streaming application, video adaptation techniques are intended to cope with the environmental variations by utilizing manipulations of the video content itself. Scalable video coding (SVC) schemes, like that suggested by the standards H.264 and its SVC extension, is highly attractive for designing a self-adaptive video streaming system. When SVC is employed in streaming system, the produced video stream can be then easily truncated or tailored to form several sub-streams which can be decoded separately to obtain a range of preferable picture size, quality and frame rate. However, questions about how to perform the adaptation using SVC and how much adaptation SVC enables are still remaining research issues. We still lack a thorough understanding of how to automate the scaling procedure in order to achieve an optimal video Quality-of-Experience for end users.</p><p>Video QoE, depends highly on human perception. In this thesis, we introduce several video QoE studies around the usability of H.264 SVC. Several factors that contribute significantly to the overall QoEs have been identified and evaluated in these studies. As an example of application usage related factor, playback smoothness and application response time are critical performance measures which can benefit from temporal scalability. Targeting on applications that requires frequent interactivity, we propose a transcoding scheme that fully utilizes the benefits of Switching P and Switching I frames specified in H.264 to enhance video stream's temporal scalability.  Focusing on visual quality related factors, a series of carefully designed subjective quality assessment tests have been performed on mobile devices to investigate the effects of multi-dimensional scalability on human quality perception. Our study reveals that QoE degrades non-monotonically with bitrate and that scaling order preferences are content-dependent. Another study find out that the flickering effect caused by frequent switching between layers in SVC compliant bit-streams is highly related to the switching period. When the period is above a certain threshold, the flickering effect will disappear and layer switching should not be considered as harmful. We have also examined user perceived video quality in 3D virtual worlds. Our results show that the avatars' distance to the virtual screen in 3D worlds contribute significant to the video QoE, i.e., for a wide extent of distortion, there exists always a feasible virtual distance from where the distortion is not detectable for most of people, which makes sense to perform video adaptation.</p><p>The work presented in this thesis is supposed to help improving the design of self adaptive video streaming services that can deliver video content independently of network technology and end-device capability while seeking the best possible experience for video.</p> / Ardendo småföretagsdoktorand

Video traffic is growing rapidly in wireless networks. Different from ordinary data traffic, video streams have higher data rates and tighter delay constraints. The ever-varying throughput of wireless links, however, cannot support continuous video playback if the video data rate is kept at a high level. To this end, adaptive video transmission techniques are employed to reduce the risk of playback interruptions by dynamically matching the video data rate to the varying channel throughput. In this dissertation, I develop new models to capture viewers' quality of experience (QoE) and design adaptive transmission algorithms to optimize the QoE. The contributions of this dissertation are threefold. First, I develop a new model for the viewers' QoE in rate-switching systems in which the video source rate is adapted every several seconds. The model is developed to predict an important aspect of QoE, the time-varying subjective quality (TVSQ), i.e., the up-to-the-moment subjective quality of a video as it is played. I first build a video database of rate-switching videos and measure TVSQs via a subjective study. Then, I parameterize and validate the TVSQ model using the measured TVSQs. Finally, based on the TVSQ model, I design an adaptive rate-switching algorithm that optimizes the time-averaged TVSQs of wireless video users. Second, I propose an adaptive video transmission algorithm to optimize the Overall Quality (OQ) of rate-switching videos, i.e., the viewers' judgement on the quality of the whole video. Through the subjective study, I find that the OQ is strongly correlated with the empirical cumulative distribution function (eCDF) of the video quality perceived by viewers. Based on this observation, I develop an adaptive video transmission algorithm that maximizes the number of video users who satisfy given constraints on the eCDF of perceived video qualities. Third, I propose an adaptive transmission algorithm for scalable videos. Different from the rate-switching systems, scalable videos support rate adaptation for each video frame. The proposed adaptive transmission algorithm maximizes the time-averaged video quality while maintaining continuous video playback. When the channel throughput is high, the algorithm increases the video data rate to improve video quality. Otherwise, the algorithm decreases the video data rate to buffer more videos and to reduce the risk of playback interruption. Simulation results show that the performance of the proposed algorithm is close to a performance upper bound. / text

There is great power in knowledge. Having insight into and predicting network events can be both informative and profitable. This thesis aims to assess how crowd-sourced network data collected on smartphones can be used to improve the quality of experience for users of the network and give network operators insight into how the networks infrastructure can also be improved. Over the course of a year, data has been collected and processed to show where networks have been performing well and where they are under-performing. The results of this collection aim to show that there is value in the collection of this data, and that this data cannot be adequately obtained without a device side presence. The various graphs and histograms demonstrate that the quantities of measurements and speeds recorded vary by both the location and time of day. It is these variations that cannot be determined via traditional network-side measurements. During the course of this experiment, it was observed that certain times of day have much greater numbers of people using the network and it is likely that the quantities of users on the network are correlated with the speeds observed at those times. Places of gathering such as malls and public areas had a higher user density, especially around noon which could is a normal time when people would take a break from the work day. Knowing exactly where and when an Access Point (AP) is utilized is important information when trying to identify how users are utilizing the network. / Graduate / davidyarish@gmail.com

Dynamic Adaptive Streaming over HTTP (DASH) is a convenient approach to transfer videos in an adaptive and dynamic way to the user. Therefore, this system makes best use of the bandwidth available. In this thesis, we investigate Dynamic Adaptive Streaming over HTTP (DASH) based on data collected from the lab experiments and user’s experiments. The objectives include investigation of how three different DASH players behave at different network conditions and up to which limit the players are tolerating the disturbances. We summarized the outcome of lab experiments on DASH at different adverse conditions and checked the lab results with user quality of experience at different adverse conditions to see up to which extent the users could tolerate the disturbances in different DASH players.

Streaming multimedia over the Internet is omnipresent but still in its infancy, specifically when it comes to theadaptation based on bandwidth/throughput measurements, clients competing for limited/shared bandwidth, and thepresence of a caching infrastructure. Nowadays the streaming infrastructure is existing over-the-top (OTT).Interestingly, these services are all delivered over-the-top of the existing networking infrastructure using the HypertextTransfer Protocol (HTTP) which resulted in the standardization of MPEG Dynamic Adaptive Streaming over HTTP(DASH).Video traffic is over 80% of the total internet traffic. Since Dynamic adaptive streaming over HTTP hasemerged it has a popular technique for video streaming on the internet. DASH allows the video player to adapt thebitrate according to the network conditions. DASH streaming client receives a manifest file, downloaded the referredvideo segments over HTTP, and play them back seamlessly emulated video streaming. This introduces latency of atleast one segment duration which decreases the quality of the user experience. In order to improve users over allQuality of Experience(QoE) a large number of adaptaion schemes were introduced to DASH.With such an importance of QoE on DASH, this thesis work investigates user acceptability of different DASHplayers. These players are exposed to different network conditions and the user analysis is given for each of thestreaming dash video for each player. The results of this thesis work include the analysis of the user rating for threedifferent players (Dash.js, GPAC and Shaka). The conclusion of this thesis is given by the quality of experience of thevideo streaming by the users which concludes that the Shaka player is preferred by the users compared to the othertwo players.

Context: In telecommunications, it is important for network providers to have a knowledge of generic relationships between multi-dimensional QoE and QoS parameters to be able to provide quality service to the customers, keeping in mind the real-time constraints such as time, money and labor. So far, there have been several research works on formulating a generic quantitative relationship between a single QoE and a single QoS parameter in literature. As per the research conducted, the most common examples of mapping between a QoS parameter and QoE were found to be the exponential model (the IQX hypothesis), the logarithmic model (the WeberFechner law), and the power model. However, it has been less common to study the multi-dimensional relationship between QoE and QoS parameters. Objective: The purpose of this paper here is to discuss the impact of several QoS parameters on QoE. The proposal put forth by existing literature is that a multiplicative model better explains the impact of QoS parameters on the overall quality as perceived by the user. The proposal was, however, never backed by subjective data. Method: We have performed several subjective tests in this regard to test our hypothesis. Non-adaptive streaming of videos in a monitored server-client setup was used. In these tests, the objective was to obtain the Mean Opinion Scores(MOS) for varying QoS parameters such as the initial delay and the number of stalls. Network shaping was used for introducing the disturbances in the videos. The experimental setup consisted of a total of 27 experiments per user and each user was handed over a questionnaire. The questionnaire mainly consisted of four questions aimed at gathering feedback from the users regarding the quality of the videos shown to them. Users were asked to mark their MOS on a continuous scale. The videos were subjected to three different values of Initial Delay, Stalls and Resolution, each. The average duration per stalls throughout the experiments was maintained at 2 seconds. Results: Data was collected from 15 users. Thus, in total 405 MOS values were recorded for 27 combinations of Initial Delay, number of Stalls and Resolution. The impact of initial delay and stalls on the QoE as indicated by the MOS was then categorized and studied for each Resolution. With the help of regression tools in MATLAB and Solver in Excel, possible models that explain the multi-dimensional QoS-QoE relationship were studied. Conclusion: The results mostly indicated towards the multiplicative model just as proposed by the existing literature. Also, it was observed that Initial Delay alone has not much impact on the overall QoE. So, the impact of Initial Delay could be described either by an additive or a multiplicative model. However, the impact of Stalls on QoE was found to be multiplicative.

This diploma thesis focuses on the issue of evaluation VoIP services (from the perspective of QoE) and their quality in university network. It includes theoretical basics of VoIP testing, principals of QoS, and also overview of QoE/MOS measurement methods. Next part describes the test bed and methodology used for measurements and evaluating of results. Site survey results and VoIP quality measurement are evaluated at the end, followed by recommendations that can lead to better VoIP quality in MENDELU wireless network.