Context: Nowadays one of the most beneficial business in IT area is web services with huge amount of users. The key ofsuccess in these type of services is flexibility in terms of providing same quality of services (QoS) and ability of fasttroubleshooting when number of users increase rapidly. To achieve these targets, evaluation of the user satisfaction is highlyessential. Moreover it is required to link user dissatisfaction to QoS parameters in terms of troubleshooting. Objectives: The main aim the research is to find an intelligent method for evaluation of the user satisfaction. The method isproposed to estimate quality-of-experience (QoE) without asking users to send their feed back. Connecting to this aim, thesecond target is finding the definition of function in equations of QoS=function(QoE). And finally, comparison of theimpact of QoS parameters on mobile application users and web site users is the last objective. Methods: For this research a web-server for video sharing propose is designed. The users can use it via web site or anAndroid mobile application. The three main QoS parameters (Packet-loss, delay and throughput) are changed gradually. Theusers are asked to score the mobile application and web site at the same time. In parallel the traffic of web-server is capturedand analyzed. Then based on variations in mean opinion scores (MOS) and also changes in TCP flags, the proper patternsfor each of the QoS parameters is provided. In this part the QoE is linked to transport layer. For the second objective, theQoE is directly linked to QoS. On the other words the graphs with QoE as horizontal axis and one of the QoS parameters asvertical axis are provided. And finally based on the gradient of these trends, the amount of impact of QoS parameters onmobile application users and web site users is compared. Results: Based on the results of the research, decrement in SYN and FIN flags and increment in ACK is an alarm for downgoing user satisfaction. In this situation, the problem is belongs to packet-loss. Increasing in the percentage of SYN is alsoa signal for user dissatisfaction. In this case, the problem is result of delay. And finally if the web-server problem is aboutthroughput then, SYN, FIN and ACK has up going trends. In all of the cases the rest of TCP flags has not clear up going ordecreasing trend.The correlation between QoS and QoE is formulated. The trends of MOS relative to QoS parameters for mobile phone andlaptop are very similar in case of packet-loss. For throughput the mobile phone users are a little more sensitive. The mostsignificant difference between the MOS values for mobile application and web site is belongs to delay. The increment indelay has really big negative effect on mobile application users. Conclusion: The final method for user satisfaction evaluation is based on the way of variations in the TCP flags. Among allthe flags, SYN, FIN and ACK passed the criteria to make the patterns. Moreover the method indicate the problem isbelongs to which of the QoS parameters. The correlation between QoE and QoS is formulated. And finally according tothese formulas, two separate web-servers for mobile application and web site is recommended.

Recent advancement in cameras and image processing technology has generated a paradigm shift from traditional 2D and 3D video to Multi-view Video (MVV) technology, while at the same time improving video quality and compression through standards such as High Efficiency video Coding (HEVC). In multi-view, cameras are placed in predetermined positions to capture the video from various views. Delivering such views with high quality over the Internet is a challenging prospect, as MVV traffic is several times larger than traditional video since it consists of multiple video sequences each captured from a different angle, requiring more bandwidth than single view video to transmit MVV. Also, the Internet is known to be prone to packet loss, delay, and bandwidth variation, which adversely affects MVV transmission. Another challenge is that end users’ devices have different capabilities in terms of computing power, display, and access link capacity, requiring MVV to be adapted to each user’s context. In this paper, we propose an HEVC Multi-View system using Dynamic Adaptive Streaming over HTTP (DASH) to overcome the above mentioned challenges. Our system uses an adaptive mechanism to adjust the video bitrate to the variations of bandwidth in best effort networks. We also propose a novel scalable way for the Multi-view video and Depth (MVD) content for 3D video in terms of the number of transmitted views. Our objective measurements show that our method of transmitting MVV content can maximize the perceptual quality of virtual views after the rendering and hence increase the user’s quality of experience.

L'utilisation répandue des smartphones dans notre vie quotidienne et l'essor technologique que connait le monde aujourd'hui -offrant l'accès mobile à très haut débit- ont exponentiellement augmenté la demande sur les services de vidéo streaming mobile, ce qui justifie la tendance à explorer de nouvelles approches pour la distribution des contenus média. Afin d'assurer une qualité de streaming constante et acceptable, la majorité des approches proposent aujourd'hui d'adapter la distribution des flux média au contexte de l'utilisateur. Dans le but de garder l'utilisateur le plus longtemps connecté à sa session de streaming, ces approches s'intéressent plus particulièrement à l'amélioration de sa perception de la vidéo. Ce qui justifie l'intérêt croissant accordé à l'étude la qualité d'expérience (QoE). Pour assurer une bonne QoE, les solutions de vidéo streaming mobile exigent la connaissance au préalable du contexte de l'utilisateur, comme par exemple la capacité de son lien physique ou la disponibilité de sa bande passante. L'acquisition de telles informations contextuelles est devenue possible aujourd'hui grâce à l'utilisation des capteurs sans fils dans les appareils mobiles et à l'existence de plusieurs applications intelligentes dédiées, le principe étant majoritairement d'exploiter la forte corrélation entre le contexte de l'utilisateur et sa position géographique. Pour faciliter l'étude du contexte de l'utilisateur, plusieurs cartes radio ont été conçues, permettant le traçage spatio-temporel des caractéristiques radio comme par exemple le débit moyen ou la force moyenne du signal. En outre, plusieurs études menées sur les modèles de mobilité des usagers ont exhibé une quasi-régularité spatio-temporelle dans leurs trajets quotidiens, soit en prenant les transports publics ou en allant vers des endroits fréquemment visités. Couplés avec les cartes radio, ces études permettent une haute précision dans la prédiction du contexte de l'utilisateur le long de son trajet. Dans cette thèse, nous nous intéressons à analyser l'impact de l'adaptation du service vidéo streaming au contexte de l'utilisateur sur sa QoE finale. Nous commençons par proposer CAMS (Context Aware Mode Switching), un mécanisme d'allocation de ressources qui dépend du contexte et qui s'applique à la distribution du vidéo streaming réel (non-adaptatif), pour assurer le minimum d'interruptions de vidéo. CAMS est conçu pour être déployé dans une topologie de réseau spécifique avec un modèle de mobilité particulier. Par la suite, nous explorons l'impact de la connaissance à l'avance du débit futur de l'utilisateur sur l'adaptation de la qualité de sa vidéo et sur le coût de sa transmission dans un contexte de streaming adaptatif. Nous proposons NEWCAST (aNticipating qoE With threshold sCheme And aScending biTrate levels), un algorithme proactif pour l'ajustement du coût et l'adaptation de la qualité sous réserve d'une prédiction parfaite du débit. Nous étendons cette étude, dans un deuxième temps, pour le cas où la prédiction du débit est imparfaite. Nous proposons, donc, d'autres algorithmes adaptatifs en nous inspirant de l'approche de NEWCAST. Pour étudier la faisabilité de ces algorithmes sur le plan pratique, nous menons quelques expérimentations dans un environnement émulé à l'aide du lecteur média DASH-IF-Reference. Finalement, nous explorons l'idée de coupler la connaissance parfaite du débit futur de l'utilisateur avec l'usage d'un mécanisme d'apprentissage automatique, pour améliorer la QoE dans un contexte de streaming adaptatif. Nous proposons, donc, un système à boucle fermée, basé sur le retour des utilisateurs, pour apprendre progressivement leurs préférences et pour optimiser adéquatement la transmission des futures vidéos. Ce système est particulièrement conçu pour être utilisé dans des populations hétérogènes avec des profils de QoE différents et inconnus à l'avance. / The strong emergence of smartphones on human daily life as well as the highbroadband access supplied by operators have triggered pervasive demand on videostreaming mobile services, requiring the exploration of novel approaches on videocontent delivery. To afford video streaming services at sustainable quality, the idea of adjusting the streaming to the time-varying users’ contexts has been actively investigatedduring the recent years. Since the users’ perceptions on the video qualitydirectly impact their engagement in video streaming sessions, many interests havebeen accorded to the user’s Quality of Experience (QoE).Today streaming solutions mostly rely on the user’s contextual information suchas his link capacity or his available bandwidth to provide an acceptable final QoE.Such contextual information can be easily acquired thanks to the existence of wirelesssensors and dedicated smart applications on today mobile devices. At the core, liesthe idea of exploiting the strong correlation between users’ locations and contexts. Tothat end, radio maps with historical average signal strength have been geographicallymapped. Various studies on users’ mobility patterns also showed that people dailyroutes exhibit a high degree of spatial and temporal regularity, especially on publictransportation or on road ways to/from frequently visited places. Coupled with radiomaps, these mobility patterns can give high accuracy on context predictability alongusers’ trips.In this thesis, we analyse the impact of adapting video streaming to the user’scontext on the final QoE.We start by proposing CAMS (Context Aware Mode Switching),a context-aware resource allocation mechanism, for real (i.e, non adaptive) videostreaming delivery to reduce the number of video stalling. CAMS is designed to beapplied in a particular network topology under a particular mobility of users. Then,we explore the impact of knowing the future throughput variations on video quality adaptation and delivery cost in adaptive video streaming. We propose NEWCAST(aNticipating qoE With threshold sCheme And aScending biTrate levels) as a proactivealgorithm for cost adjustment and quality adaptation under the assumption of aperfect throughput estimation. We then extend the study to the case where throughputprediction errors may exist and propose a bench of adaptive algorithms inspiredfrom NEWCAST. To explore the feasibility of implementing these algorithms in realworld streaming, we conduct some experiments with the DASH-If Reference playerin an emulated environment. Finally, we explore the impact of knowing the futurethroughput variations when exploited with machine learning on the global QoE enhancementin adaptive video streaming. We propose a closed-loop framework basedon users’ feedbacks to progressively learn their QoE profiles and to fittingly optimizevideo deliveries. This framework is in particular suited for heterogenous populationswhere the QoE profiles of users are quite different and unknown in advance.

Smartphones have become crucial enablers for users to exploit online services such as learning, leisure, communicating, and socializing. The user-perceived quality of applications and services is an important factor to consider, in order to achieve lean resource management, to prevent user churn and revenue depletion of service or network providers. This is often studied within the scope of Quality of Experience (QoE), which has attracted researchers both in academia and industry. The objective of this thesis is to study the most important factors influencing QoE on smartphones and synthesize solutions for intervention. The temporal impairments during a real-time energy-hungry video streaming are studied. The aim is to quantify the influence of temporal impairments on the user-perceived video QoE at the network and application level together with energy measurements, and also to propose solutions to reduce smartphone energy consumption without degrading the user’s QoE on the smartphone for both user-interactive, e.g., video, and non-interactive cases. QoE measurements on smartphones are performed throughout in-the-wild user studies. A set of quantitative Quality of Experience (QoE) assessment tools are implemented and deployed for automatic data logging at the network- and application-level. Online momentary survey, Experience Sampling Method (ESM) software, and Day Reconstruction Method (DRM) along weekly face-to-face user interviews are employed. The subjective QoE is obtained through qualitative feedback including Mean Opinion Score (MOS) as well as in-situ indications of poor experiences by users. Additionally, energy measurements on smartphones are conducted in controlled-lab environment with the Monsoon device. The QoE of smartphone applications and services perceived by users depends on many factors including anomalies in the network, application, and also the energy consumption. At the network-level, high packet delay variation causes long video freezes that eventually impact negatively the end-user perceived quality. The freezes can be quantified as large time gaps in-between the displayed pictures during a video stream at the application-level. We show that the inter-picture time in cellular-based video stream can be represented via two-state exponential ON/OFF models. We show models representing the non-linear relationship between the QoE and the mean inter-picture time. It is shown that energy measurements help to reveal the temporal impairments in video stream enabling energy consumption as a QoE indicator. Next, energy waste and saving during temporal impairments are identified. Additionally, other video streaming use cases, e.g., “download first and watch later”, are studied and appropriate energy-saving download scheduling mechanisms are recommended. The possibility for decreasing energy consumption when the smartphone screen is OFF, while maintaining QoE, is revealed. We first show exponential models to represent user’s interaction with smartphone, then propose a NyxEnergySaver software, to control the cellular network interface in a personalized manner to save smartphone energy. According to our findings, more than 30% smartphone energy can be saved without impacting the user-perceived QoE.

Video streaming has become, and is expected to remain, the dominant type of traffic over the Internet. With this high demand for multimedia streaming, there is always a question on how to provide acceptable and fair Quality of Experience (QoE) for consumers of the over-the-top video services, despite the best-effort nature of the Internet and the limited network resources, shared by concurrent users. MPEG-DASH, as one of the most widely used standards of HTTP-based adaptive streaming, uses a client-side rate adaptation algorithms; which is known to suffer from two practical challenges: in one hand, clients use fixed heuristics that have been fine-tuned according to strict assumptions about deployment environments which limit its ability to generalize across network conditions. On the other hand, the absence of collaboration among DASH clients leads to unfair bandwidth allocation, and typically ends up in an unbalanced equilibrium point. We believe that augmenting a server-side rate adaptation significantly improves the fairness of network bandwidth allocation among concurrent users. We have formulated the problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) model, and used RL to train two neural networks to find an optimal solution to the proposed Dec-POMDP problem in a distributed way. We showed that our proposed client-server collaboration outperforms the state-of-the-art schemes in terms of QoE-efficiency, QoE-fairness, and social welfare by as much as 16%, 21%, and 24% respectively.

Mobile communications has gained a growing interest from both customers and service providers alike in the last 1-2 decades. Visual information is used in many application domains such as remote health care, video –on demand, broadcasting, video surveillance etc. In order to enhance the visual effects of digital video content, the depth perception needs to be provided with the actual visual content. 3D video has earned a significant interest from the research community in recent years, due to the tremendous impact it leaves on viewers and its enhancement of the user’s quality of experience (QoE). In the near future, 3D video is likely to be used in most video applications, as it offers a greater sense of immersion and perceptual experience. When 3D video is compressed and transmitted over error prone channels, the associated packet loss leads to visual quality degradation. When a picture is lost or corrupted so severely that the concealment result is not acceptable, the receiver typically pauses video playback and waits for the next INTRA picture to resume decoding. Error propagation caused by employing predictive coding may degrade the video quality severely. There are several ways used to mitigate the effects of such transmission errors. One widely used technique in International Video Coding Standards is error resilience. The motivation behind this research work is that, existing schemes for 2D colour video compression such as MPEG, JPEG and H.263 cannot be applied to 3D video content. 3D video signals contain depth as well as colour information and are bandwidth demanding, as they require the transmission of multiple high-bandwidth 3D video streams. On the other hand, the capacity of wireless channels is limited and wireless links are prone to various types of errors caused by noise, interference, fading, handoff, error burst and network congestion. Given the maximum bit rate budget to represent the 3D scene, optimal bit-rate allocation between texture and depth information rendering distortion/losses should be minimised. To mitigate the effect of these errors on the perceptual 3D video quality, error resilience video coding needs to be investigated further to offer better quality of experience (QoE) to end users. This research work aims at enhancing the error resilience capability of compressed 3D video, when transmitted over mobile channels, using Multiple Description Coding (MDC) in order to improve better user’s quality of experience (QoE). Furthermore, this thesis examines the sensitivity of the human visual system (HVS) when employed to view 3D video scenes. The approach used in this study is to use subjective testing in order to rate people’s perception of 3D video under error free and error prone conditions through the use of a carefully designed bespoke questionnaire.

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

The significant growth in multimedia services and traffic (e.g. VoIP, video streaming and video gaming) in current and emerging mobile networks including the latest 4G Long-Term Evolution (LTE) networks and the rising user expectation for high Quality of Experience (QoE) for these services have posed real challenges to network operators and service providers. One of the key challenges is how to bring multimedia services to the end-user over resource-constrained mobile networks with a satisfactory QoE. Cost-effective solutions are needed for network operators to improve the bandwidth usage of these mobile networks. Therefore, scheduling schemes are of extreme importance in LTE, where scheduling algorithms are responsible for the overall efficiency of resource allocation in an LTE system. The aim of the project is to develop novel QoE-driven scheduling algorithms for improving system capacity in delivering multimedia services over downlink 3GPP LTE. This is to move away from traditional QoS-driven scheduling schemes to a QoE-driven scheme which guarantee end-user satisfaction in resource allocation. The main contributions of the thesis are threefold: 1. Performance of several existing scheduling algorithms for VoIP applications was evaluated thoroughly in terms of QoE metric (i.e. MOS), instead of QoS metrics (e.g. packet loss and delay). Using QoE metrics instead of QoS ones will facilitate the development of QoE-driven scheduling schemes in order to achieve optimised end-user experiences or optimised mobile system capacity. 2. A novel QoE-driven LTE downlink scheduling scheme for VoIP application was developed to maximize the number of users per cell at an acceptable MOS score. The proposed scheme achieved significant improvement in cell capacity at an acceptable quality (75% compared to MLWDF, and 250% compared to PF and EXP-PF in all three lower speed scenarios considered). 3. A QoE-driven LTE downlink scheduling scheme for multiservice multimedia applications was developed to improve the cell capacity with satisfactory QoE for both VoIP and video streaming services. The proposed algorithm performed well in a pedestrian scenario increasing cell capacity to double for video stream with ‘Rapid Movement’ (RM) content. For ‘Medium Movement’ (MM) video content, the capacity was increased about 20% compared to MLWDF and by 40% compared to EXP-PF. In a vehicular scenario, the proposed scheme managed to enhance the cell capacity for MM video stream case. The project has led to three publications (IEEE Globecom’12 – QoEMC Workshop, IEEE CCNC’15 and IEEE MMTC E-letter/May-2015). A journal paper is in preparation.

Video streaming over the best-effort networks is a challenging problem due to the time-varying and uncertain characteristics of the links. When multiple video streams are present in a network, they share and compete for the common bandwidth. In such a setting, a bandwidth allocation algorithm is required to distribute the available resources among the streams in a fair and efficient way. Specifically, it is desired to establish fairness across end-users' Quality of Experience (QoE). In this research, we propose three novel methods to provide QoE-fair network bandwidth allocation among multiple video streaming sessions. First, we formulate the problem of bandwidth allocation for video flows in the context of Network Utility Maximization (NUM) framework, using sigmoidal utility functions, rather than conventional but unrealistic concave functions. An approximation algorithm for Sigmoidal Programming (SP) is utilized to solve the resulting nonconvex optimization problem, called NUM-SP. Simulation results indicate improvements of at least 60% in average utility/QoE and 45% in fairness, while using slightly less network resources, compared to two representative methods. Subsequently, we take a collaborative decision-theoretic approach to the problem of rate adaptation among multiple video streaming sessions, and design a multi-objective foresighted optimization model for network resource allocation. A social welfare function is constructed to capture both fairness and efficiency objectives at the same time. Then, assuming a common altruistic goal for all network users, we use multi-agent decision processes to find the optimal policies for all players. We propose a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) model for the conventional IP networks and a Multi-agent Markov Decision Process (MMDP) model for the SDN-enabled wireless networks. By planning these cooperative decision process models, we find the optimal network bandwidth allocation that leads to social welfare maximization. Distributed multi-agent reinforcement learning algorithms are also designed and proposed as a low-complexity model-free solution to these optimization problems. Simulations of the proposed methods show that the resulting optimal policies of the novel Social Utility Maximization (SUM) framework outperform existing approaches in terms of both efficiency and fairness. The Dec-POMDP model applied to a server-side rate adaptation results in 25% improvement in efficiency and 13% improvement in fairness, compared to one popular protocol of congestion control for multimedia streaming. Our performance evaluations also show that the MMDP model applied to a client-side rate adaptation like DASH improves efficiency, fairness, and social welfare by as much as 18%, 24%, and 25%, respectively compared to current state-of-the-art.

No description available.