Home networks are evolving vigorously and are also becoming more and more heterogeneous and complex. Home networks contain new services and numerous devices with wired and wireless links. At the same time, users demand high levels of quality of service for many new applications. Hence, the supervision of links quality tends to be mandatory in emergent home networks, to trigger QoS mechanisms. In this context, this dissertation proposes the utilization of the available bandwidth as a performance indicator in hybrid home networks (e.g. for remote service-evaluation, diagnosis and fault detection) and as link-state metric for a number of QoS mechanisms (e.g., admission control, path selection and load balancing). In this dissertation, we firstly explain why available bandwidth probing is a fundamental tool in the new QoS architectures for hybrid home networks and we explore different use cases. We investigate the networking constraints that affect bandwidth probing in hybrid home networks. We explain why most tools to measure available bandwidth on Internet paths are not very efficient on home networks. We make a taxonomic study of a number of state-of-the-art probing techniques and probing tools for available bandwidth. At that aim, we propose a functional framework, called Metrics Profile Capture Filter and Feedback (MPCFF). Based on these studies, we choose Iperf in TCP mode as an attractive tool to estimate available bandwidth. Then, we conduct a performance evaluation on an experimental test bed, to compare Iperf in lightweight TCP-mode vs. various state-of-the-art tools (Wbest, Pathchirp, Pathload and IGI/PTR). We show that Iperf in lightweight TCP mode is superior in terms of accuracy and speed of convergence. Finally, we integrate Iperf in lightweight TCP mode into a path selection protocol using the Inter-MAC software (developed in the Omega European project). We show, on a test bed, how Iperf in lightweight TCP mode enhances the Inter-MAC path selection by eliminating performance deterioration due to time-varying capacity links. Our results show that Iperf in lightweight TCP mode, accurately, triggers path selection and load balancing mechanisms, to adapt the network resources to the strong degradation of WiFi and PLC links. Available bandwidth probing detects and helps to control real-time deviations of the quality of experience, associated of sensitive applications. At the same time, available-bandwidth probing helps the service provider to isolate failures and to monitor the end-to-end residential service, on demand or on long-term periodicity basis.

Network Utility Maximization (NUM) provides a key conceptual framework to study reward allocation amongst a collection of users/entities in disciplines as diverse as economics, law and engineering. However when the available resources and/or users' utilities vary over time, reward allocations will tend to vary, which in turn may have a detrimental impact on the users' overall satisfaction or quality of experience. In this thesis, we introduce a generalization of the NUM framework which incorporates the detrimental impact of temporal variability in a user's allocated rewards and explicitly incorporates Mean-Variability-Fairness tradeoffs, i.e., tradeoffs amongst the mean and variability in users' reward allocations, as well as fairness across users. We propose a simple online algorithm to realize these tradeoffs, which, under stationary ergodic assumptions, is shown to be asymptotically optimal, i.e., achieves a long term performance equal to that of an offline algorithm with knowledge of the future variability in the system. This substantially extends work on NUM to an interesting class of relevant problems where users/entities are sensitive to temporal variability in their service or allocated rewards. We extend the theoretical framework and tools developed for realizing Mean-Variability-Fairness tradeoffs to develop a simple online algorithm to solve the problem of optimizing video delivery in networks. The tremendous increase in mobile video traffic projected for the future along with insufficiency of available wireless network capacity makes this one of the most important networking problems today. Specifically, we consider a network supporting video clients streaming stored video, and focus on the problem of jointly optimizing network resource allocation and video clients' video quality adaptation. Our objective is to fairly maximize video clients' video Quality of Experience (QoE) realizing Mean-Variability-Fairness tradeoffs, incorporating client preferences on rebuffering time and the cost of video delivery. We present a simple asymptotically optimal online algorithm NOVA (Network Optimization for Video Adaptation) to solve the problem. Our algorithm uses minimal communication, 'distributes' the tasks of network resource allocation to a centralized network controller, and video clients' video quality adaptation to the respective video clients. Further, the quality adaptation is also optimal for standalone video clients, and is an asynchronous algorithm well suited for use in the Dynamic Adaptive Streaming over HTTP (DASH) framework. We also extend NOVA for use with more general video QoE models, and study NOVA accounting for practical considerations like time varying number of video clients, sharing with other types of traffic, performance under legacy resource allocation policies, videos with variable sized segments etc. / text

Mobile Video streaming has become popular with the arrival of high capability smart phones on Mobile Networks. Users are streaming high definition (HD) videos and have high expectations on Mobile Quality of Experience (QoE). Fourth Generation Mobile Networks are being deployed recently in many regions of the world and have capability to efficiently handle high definition video traffic. YouTube is one of the most extensively used Video on Demand Service on smart phones where wide range of people do video streaming. Most of the QoE experiments on 4g networks have been conducted in lab environment but they do not capture the daily routine of user experience on viewing videos. The user experiments in this thesis are conducted in natural life settings to capture Quality of Experience based on context of the user location. User experiments are designed and conducted for collecting Mean Opinion scores (MOS) in four different context based locations and have been collected using Experience Sampling Method (ESM). The Contribution of thesis is applying linear mixed effect Modeling on data collected to develop Quality of Experience (QoE) models of video streaming for Mobile YouTube on 4G Network. This thesis is one of the first works on modeling QoE experience at different context based physical locations in natural settings for mobile YouTube video streaming in 4G networks.

To be successful in today's competitive market, service providers should look at user's satisfaction as a critical key. In order to gain a better understanding of customers' expectations, a proper evaluations which considers intrinsic characteristics of perceived quality of service is needed. Due to the subjective nature of quality, the vagueness of human judgment and the uncertainty about the degree of users' linguistic satisfaction, fuzziness is associated with quality of experience. Considering the capability of Fuzzy logic in dealing with imprecision and qualitative knowledge, it would be wise to apply it as a powerful mathematical tool for analyzing the quality of experience (QoE). This thesis proposes a fuzzy procedure to evaluate the quality of experience. In our proposed methodology, we provide a fuzzy relationship between QoE and Quality of Service (QoS) parameters. To identify this fuzzy relationship a new term called Fuzzi ed Opinion Score (FOS) representing a fuzzy quality scale is introduced. A fuzzy data mining method is applied to construct the required number of fuzzy sets. Then, the appropriate membership functions describing fuzzy sets are modeled and compared with each other. The proposed methodology will assist service providers for better decision-making and resource management.

Nowadays, Third Generation (3G) mobile phones equipped with powerful hardware are becoming popular and dominating the market of cellular com-munication systems. Features such as music and video players, in-built Global Positioning System (GPS) receivers, navigation maps, Internet connectivity and high resolution cameras has converted mobile phones into so called Smartphones. With the increase in applications and services, limitations on energy consumption are also increasing. Therefore, it is more im-portant for manufacturers to nd e ective means of increasing battery life of mobile phones, as the mobile device itself is energy consuming and longer operational times are demanded by customers. Good energy mangement in mobile phones requires a good understanding of the energy usage in mobile phones. To this end, this thesis report presents the results of power and energy consumption measurements conducted on available Smartphones. The services under investigation includes basic Smartphone functionalities and few Internet services are studied and conclusions were drawn. This paper presents a cost e ective methodology for reliable measurements of energy on Smartphones. It also examines the e ect of operating systems on en-ergy consumption in mobile phones and reports less energy consumption for Android supported phones. The energy consumption of these Smartphones with respect to various applications has been studied and related to Quality of Experience of users'. Based on the results conclusions were drawn. / This Thesis will serve as a guide to manufacturers and Users of smartphones while choosing smartphones and designing energy-efficient protocols. / +46 (0)723260656

L'émergence et la croissance rapide des services multimédia dans les réseaux IP ont créé de nouveaux défis pour les fournisseurs de services réseau, qui, au-delà de la Qualité de Service (QoS) issue des paramètres techniques de leur réseau, doivent aussi garantir la meilleure qualité de perception utilisateur (Quality of Experience, QoE) dans des réseaux variés avec différentes technologies d'accès. Habituellement, différentes méthodes et techniques sont utilisées pour prédire le niveau de satisfaction de l'utilisateur, en analysant l'effet combiné de multiples facteurs. Dans cette thèse, nous nous intéressons à la commande du réseau en intégrant à la fois des aspects qualitatifs (perception du niveau de satisfaction de l'usager) et quantitatifs (mesure de paramètres réseau) dans l'objectif de développer des mécanismes capables, à la fois, de s'adapter à la variabilité des mesures collectées et d'améliorer la qualité de perception. Pour ce faire, nous avons étudié le cas de deux services multimédia populaires, qui sont : le streaming vidéo, et la voix sur IP (VoIP). Nous investiguons la QoE utilisateur de ces services selon trois aspects : (1) les méthodologies d'évaluation subjective de la QoE, dans le cadre d'un service vidéo, (2) les techniques d'adaptation de flux vidéo pour garantir un certain niveau de QoE, et (3) les méthodes d'allocation de ressource, tenant compte de la QoE tout en économisant l'énergie, dans le cadre d'un service de VoIP (LTE-A). Nous présentons d'abord deux méthodes pour récolter des jeux de données relatifs à la QoE. Nous utilisons ensuite ces jeux de données (issus des campagnes d'évaluation subjective que nous avons menées) pour comprendre l'influence de différents paramètres (réseau, terminal, profil utilisateur) sur la perception d'un utilisateur d'un service vidéo. Nous proposons ensuite un algorithme de streaming vidéo adaptatif, implémenté dans un client HTTP, et dont le but est d'assurer un certain niveau de QoE et le comparons à l'état de l'art. Notre algorithme tient compte de trois paramètres de QoS (bande passante, taille de mémoires tampons de réception et taux de pertes de paquets) et sélectionne dynamiquement la qualité vidéo appropriée en fonction des conditions du réseau et des propriétés du terminal de l'utilisateur. Enfin, nous proposons QEPEM (QoE Power Efficient Method), un algorithme d'ordonnancement basé sur la QoE, dans le cadre d'un réseau sans fil LTE, en nous intéressant à une allocation dynamique des ressources radio en tenant compte de la consommation énergétique / The emerging and fast growth of multimedia services have created new challenges for network service providers in order to guarantee the best user's Quality of Experience (QoE) in diverse networks with distinctive access technologies. Usually, various methods and techniques are used to predict the user satisfaction level by studying the combined impact of numerous factors. In this thesis, we consider two important multimedia services to evaluate the user perception, which are: video streaming service, and VoIP. This study investigates user's QoE that follows three directions: (1) methodologies for subjective QoE assessment of video services, (2) regulating user's QoE using video a rate adaptive algorithm, and (3) QoE-based power efficient resource allocation methods for Long Term Evaluation-Advanced (LTE-A) for VoIP. Initially, we describe two subjective methods to collect the dataset for assessing the user's QoE. The subjectively collected dataset is used to investigate the influence of different parameters (e.g. QoS, video types, user profile, etc.) on user satisfaction while using the video services. Later, we propose a client-based HTTP rate adaptive video streaming algorithm over TCP protocol to regulate the user's QoE. The proposed method considers three Quality of Service (QoS) parameters that govern the user perception, which are: Bandwidth, Buffer, and dropped Frame rate (BBF). The BBF method dynamically selects the suitable video quality according to network conditions and user's device properties. Lastly, we propose a QoE driven downlink scheduling method, i.e. QoE Power Escient Method (QEPEM) for LTE-A. It esciently allocates the radio resources, and optimizes the use of User Equipment (UE) power utilizing the Discontinuous Reception (DRX) method in LTE-A

The idiom by Thomas Fuller fantasizes the fact that seeing is believing, but the feeling is the truth. This ideology has fired the vision and innovation of the Mulsemedia, multiple-sensorial media, and Internet of Skills (IoS) which enable the exchange of control, skills, and expertise anytime/everywhere across the Internet. With the emergence of the new generation of mobile network (5G), Tactile Internet, as well as the deployment of Industry 4.0 and Health 4.0, multimedia systems are moving towards immersed haptic enabled human-machine interaction systems such as the Digital Twin (DT). Specifically, Industry 4.0 will be using DT and robots on a large scale. This will increase human-machine and interaction to a great extent. There will be multimodal communications used to interact with digital twins and robots, specially haptics. Hence, tactile internet will replace the conventional internet today. In fact, a DT system can also be extended in Health 4.0 domain to act as a COVID-19 early warning system. Tracking a person’s temperature and other symptom data in real-time can signal if as well as when it’s time to see a doctor or take a COVID test. Link to a COVID tracing app, the digital twin might help get more information about the virus relative to the person itself. Since there are currently no well-recognized models to evaluate the performance of these systems, to address this research lacuna, we proposed a Quality-of-Experience (QoE) model for DT systems containing multi-levels of subjective, objective, and physiopsychological influencing factors. The model is itemized through a fully detailed taxonomy that deduces the perceived user’s emotional and physical states during and after consuming spatial, temporal, proximal, and abstracted multi-modality media between humans and machines. Further, the taxonomy was modelled using the best practice of machine learning methods to show how QoE for digital twin applications can be inferred and predicted from interactions and biosignals in this class of applications. Furthermore, the taxonomy was applied to two use cases. The first one addresses the objective quality optimization for transmission in a large scale immersed haptic virtual reality over the Internet while the second one aims to objectively infer an important DT QoE physiological aspect i.e, fatigue.

Master thesis involves the development of quality measurement issues and performance parameters in data networks. It describes the main technologies as they affect the quality and performance parameters and the effect of these parameters for voice, video and data services. Next are listed some methods for measuring parameters of the data network.In the practical part is selected one method of measuring network parameters and properties of this method are demonstrated by illustrative examples.

Master thesis involves the development of quality measurement issues and performance parameters in data networks. It describes the main technologies as they affect the quality and performance parameters and the effect of these parameters for voice, video and data services. Next are listed some methods for measuring parameters of the data network.In the practical part is selected one method of measuring network parameters and properties of this method are demonstrated by illustrative examples.

User Generated Content (UGC) has boosted a high popularity since the birth of a wide range of web services allowing the distribution of such user-produced media content, whose patterns vary from textual information, photo galleries to videos on site. The boom of Internet of Things and the newly released HTML5 accelerate the development of multimedia patterns as well as the technology of distributing it. YouTube, as one of the most popular video sharing site, enjoys the top most numbers of video views and video uploads per day in the world. With the rapid growing of multimedia patterns as well as huge bandwidth demand from subscribers, the sheer volume of the traffic is going to severely strain the network resources.</p><p>Therefore, analyzing media streaming traffic patterns and cacheability in live IP-access networks today leads a hot issue among network operators and content providers. One possible solution could be caching popular contents with a high replay rate in a proxy server on LAN border or in users' terminals.</p><p>Based on the solution, this thesis project focuses on developing a measurement framework to associate network cacheability with video category and video duration under a typical Swedish municipal network. Experiments of focused parameters are performed to investigate potential user behavior rules. From the analysis of the results, Music traffic gets a rather ideal network gain as well as a remarkable terminal gain, indicating that it is more efficient to be stored close to end user. Film&Animation traffic, however, is preferable to be cached in the network due to its high net gain. Besides, it is optimal to cache the video clips with a length between 3 and 5 minutes, especially the Music and Film&Animation traffic. In addition, more than half of the replays occur during 16.00-24.00 and peak hours appear on average from 18.00 to 22.00. Lastly, only around 16% of the videos are global popular and very few heavy users tend to be local popular video viewers, depicting local limits and independent user interests