Visitors to parks and protected areas (PPAs) engage in a variety of activities and choose different modes of travel, especially when the location itself has few limitations, such as open-water settings with relatively few backcountry visitors. Managers must understand why visitors are recreating in a particular place and the quality of their experiences in order to offer appropriate and meaningful opportunities. This study seeks to better understand visitor motivations (the “why”), to develop and measure effective indicators for evaluating the quality of visitor experiences, and to contextualize these findings with a unique investigation of historical Glacier Bay National Park data. This study contributes to current literature by exploring visitor dimensions in a coastal Alaskan park. Visitor intercept surveys were conducted for six different visitor groups. Several statistical analyses were completed, resulting in eight visitor motivations, a three-group clustering of visitors based on their motivations, and an overall report of high-quality experiences. Historical comparisons confirm that decades later visitors continue to be motivated by opportunities to experience glaciers, solitude, and natural connection and renewal; litter, cruise ships and propeller-driven aircrafts continue to be the main social factors detracting from the visitor’s overall experience; and visitors are shifting to older, highly educated, wealthy travelers.

The viewing experience of news videos differ depending on if the device used is a smartphone or a traditional TV, e.g. because some social situations for smartphones not allow audio to be used while watching. Can the adding of subtitles keep the perceived quality at the same level even in these situations? This study investigates the research question how the viewing experience of news videos on smartphones is affected by subtitles and text for young adults. The results are relevant for anyone aiming to improve the viewing experience of videos on smartphones. The study is based on the theory about the concept Quality of Experience, QoE. The method has an experimental approach: 64 participants watched three news videos and after subjective and objective QoE was measured. The conclusion is that subtitles increased every aspect of subjective and objective QoE in a public context when the videos were watched without audio, while they in a home context where audio was present, did not affect any aspect of QoE, except for a decreased willingness to continue watching. / En tittarupplevelse varierar beroende på om tittandet görs på en smartphone eller på en traditionell TV, bland annat eftersom några tittarsituationer på smartphones socialt sett inte tillåter användning av ljud under tittandet. Kan undertexter göra att den upplevda kvalitén behålls på motsvarande nivå även i dessa situationer? Denna studie undersöker frågeställningen hur tittarupplevelsen av nyhetsklipp på smartphones påverkas av undertexter och texter för unga vuxna. Dess resultat är relevanta för alla som ämnar förbättra tittarupplevelsen på smartphones. Studien är baserad på teori om begreppet ”Quality of Experience”, QoE. Metoden har en experimentell ansats: 64 deltagare tittade på tre nyhetsklipp och därefter mättes den subjektiva och objektiva kvalitén på upplevelsen. Slutsatsen är att undertexter ökade varje aspekt av subjektiv och objektiv upplevd kvalité i en offentlig miljö där klippen sågs utan ljud, medan i en hemmiljö – där klippen sågs med ljud, påverkade inte undertexterna någon aspekt av kvalitén, förutom att viljan att titta klart minskade.

Following the rapid development of mobile infrastructure and hand-held user devices like smartphones and tablets, the way that mobile networks have been utilized has changed accordingly. Users are no longer using their phones just to talk to and message each other, but have migrated most of their multimedia consumption to these devices. The increased network strain and reduced profit margins that new services bring to the table might lead to future decoupling between revenues and expenses in the network. In order to avoid this scenario various different strategies have been proposed and one of them is introduction of QoS/QoE related resource allocation techniques. This thesis looks at Interruption Prediction scheduler which tries to predict and prevent audio or video interruptions experienced during video conference call. In order to test the performance of these schedulers, MATLAB simulator reusing some of the functions from RUNE toolbox has been used. Results of the newly presented algorithms are compared with a QoE/QoS agnostic scheduler. Unfortunately results of these schedulers in the case of video conference are not as promising as they were in the case of some other types of services. The research done shows that these modifications of the scheduling algorithms do not help to increase the network performance in terms of observed KPIs (Total Time of Interruptions, Frequency of Interruptions and network throughput) in most of the described scenarios. However, there are some special scenarios when these schedulers show a certain potential.

Mobile data traffic will continue to show rapid growth in the coming years; however the data revenue is not rising fast enough to ensure the operators’ profitability. Therefore, mobile operators must seek new approaches to find out what service does the customers need and what quality makes the customers satisfied in order to keep their increasingly sophisticated customers satisfied at the same time minimizing their revenue gap. This paper investigate the effect of mobile cellular network performance and contextual factors on smartphone users’ satisfaction, this was done via crowdsourcing through an experiment where an Android Application and a user Survey were included, which is able to evaluate and analyze the perceived quality of experience (QoE) for YouTube service for Android Smartphone users. To achieve this goal, the App NPT performs measurements of objective quality of service (QoS) parameters, whereas the survey carriers out collecting subjective user opinion. The result show that network performance parameters do impact the MOS (Mean Opinion Score) exponentially, either in a positively or negatively way, however, multiple parameters need to be considered together in order to draw a more accurate correlation with QoE. In addition, QoE are heavily affected by many other contextual factors, such as age and gender as well as users location. QoE are also impacted by several subjective factors, such as user expectation. Not always the highest throughput will lead to the best QoE, and not always the best technology (LTE) deserves the best MOS. Even though user received very high downlink throughput, their MOS value may still be low due to they might think the video were not fun to watch and the quality has not meet their expectation.

As an increasing position of the world’s communication moves towards the cloud and wireless solutions the requirement for good throughput and low delay increases. One step towards meeting higher requirements is the move from 4G Long Term Evolution (LTE) to 5G New Radio (NR). In order to utilize the potential of 5G NR, software needs to be improved. With the goal to lower the delay for delay critical applications and services when using 5G NR this thesis studies a new scheduler inspired by Earliest Deadline First (EDF) as a soft real-time system scheduler. This new scheduler called Real-Time Inspired Hybrid Scheduler (RTIHS) is proposed where two different schedulers are used depending on if the network traffic is delay critical or not. Delay critical traffic is served by the new Deadline Inspired Scheduler (DIS) scheduler and other traffic by a traditional Round Robin (RR) scheduler. The transmissions that are delay critical are prioritized differently with the use of a constant that has two functions. To determine if the transmission is in time or not, and how close the transmission is to the fixed deadline. Up until the deadline the priority of that transmission is increased with a factor that is affected by how close the current time is to the deadline. If the deadline has been missed the priority is, however, decreased with respect to how much it missed the deadline.RTIHS is implemented and tested in a state-of-the-art system simulator where services such as;Cloud Gaming (CG), Video on Demand (VoD), and web browsing are evaluated. An already existing technology named Low Latency Low Loss Scalable Throughput (L4S) is included in the evaluations to investigate how RTIHS scales. The performance of RTIHS is then compared to a Delay Scheduler(DS) and a RR scheduler that act as the baseline. The results show that RTIHS performs better for CG in terms of delay and nominal rate than the baseline, especially when the network is under high load. Using RTIHS compared to a DS shows an average increase of nominal rate by roughly 6 % and an average decrease of delay by 16 % for the average users. At the same time RTIHS does show higher delay and lower throughput for services such as VoD and web browsing, making its performance for those service worse than the baseline, especially when the network load is high. With the same comparison as before, RTIHS shows an average 17 % higher delay for web browsing and 1 % lower requested video rate than the DS for the average users. This is due to how the evaluated services are prioritized with the limited resources available. Since RTIHS prioritizes CG more, less resources remain for the other services. The baseline is not as biased towards CG and therefore has a lower nominal rate and higher delay for said service but better for the other services in comparison. Activating L4S mitigates the underwhelming performance of RTIHS for VoD and web browsing further improves the performance for CG. This is also true for the baseline which increases performance for CG and further increases the performance for other services with a small amount. With L4S activated the average increase of nominal rate in CG for RTIHS compared to the DS is 13 % for the average users and the average delay decrease is 9 %. Meanwhile the average requested video rate for RTIHS is less than 1 % lower than that of the DS for the average users and the delay for web browsing is 10 % higher than the DS for the average users.

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Aujourd'hui, la satisfaction du client et la joie deviennent les principaux différenciateurs pour évaluer l'efficacité des opérateurs télécoms et les fournisseurs de services. Dans ce marché concurrentiel, la mauvaise qualité de l'expérience (QoE) conduit à une réaction en chaîne de la parole négative de la bouche, pousser les clients dans les bras de concurrents en attente. Par conséquent, il est important pour les fournisseurs de services pour assurer la qualité supérieure de l'expérience afin d'éviter la déloyauté clientèle et la réputation négative. QoE est une émergence rapide multi-disciplinaire discipline axée sur la compréhension globale des exigences de qualité de l'homme sous différents angles tels que la technologie, des affaires et contexte d'utilisation. Le premier défi et avant tout est de comprendre comment différentes caractéristiques qui influencent liés à l'activité, la technologie, et le comportement influence du contexte humain. Dans cette thèse, les premiers travaux répond à ce défi de comprendre l'influence des domaines disparates sur QoE. Un modèle d'interaction QoE consolidée est proposé qui relie les domaines disparates (humains, des affaires, la technologie, et le contexte) pour comprendre la qualité globale de l'homme de l'expérience requise. Puis la taxonomie est présenté pour le modèle d'interaction QoE. Deuxième contribution dans cette thèse est basé sur le premier et son principal objectif est de capturer et analyser des données QoE à travers des études de l'utilisateur. Sur la base de données de l'utilisateur, l'influence des paramètres technologiques, contextuelles et d'affaires de plus de QoE sont évalués. Différents services multimédias ont été sélectionnés pour l'étude d'utilisateur tels que le service de streaming vidéo, la téléphonie (VoIP et PSTN), et service de téléconférence audio 3D. Selon le service multimédia, différents aspects ont été considérés lors de chaque étude sur les utilisateurs telles que les types de paramètres de service (QoS multimédia, le contenu, le contexte), les types de mesures (QoE subjective, objectif cognitif ou les deux), les caractéristiques de l'homme (âge, sexe, etc) , et les rôles de l'homme (l'utilisateur ou le client). Ces résultats aident à comprendre le lien entre QoE et d'autres domaines d'influence. La troisième contribution est basée sur les travaux en cours d'élaboration QoE des outils basés sur des services de streaming vidéo. Deux outils basés sur QoE pour l'évaluation des services multimédias ont été présentés dans cette thèse, leurs principales fonctions consistent à saisir, analyser et rapporter métriques QoE en temps réel. Ces outils sont utiles pour QoE mesure en temps réel de paramètres QoE / Today, the fulfillment of customer demands and user experience requirements are becoming the main differentiators for gauging the effectiveness of telecom operators and service providers. In this competitive market, the poor Quality of Experience (QoE) leads to a chain reaction of negative word of mouth, pushing customers into the arms of waiting competitors. Therefore it is important for service providers to ensure superior quality of experience in order to avoid customer disloyalty and negative reputation. QoE is a fast emerging multi-disciplinary field focused on understanding overall human quality requirements from different angles such as technology, business and context of use. The first and foremost challenge is to understand how different influencing characteristics related to business, technology, and context influence human behavior. In this thesis, initial work addresses this challenge of understanding the influence of disparate domains over QoE. A consolidated QoE interaction model is proposed which links disparate domains (human, business, technology, and context) to understand overall human quality of experience requirements. Then taxonomy is presented for QoE interaction model. Second contribution in this thesis is based on the first and its main objective is to capture and analyze QoE data through user studies. Based on user data, the influence of technological, contextual and business parameters over QoE are evaluated. Different multimedia services were selected for user study such as video streaming service, telephony (VoIP and PSTN), and 3D audio teleconferencing service. Depending upon multimedia service, different aspects were considered during each user study such as types of multimedia service parameters (QoS, content, context), the types of QoE metrics (subjective, objective cognitive or both), human characteristics (age, gender etc), and human roles (user, or customer). These findings help in understanding the link between QoE and other influencing domains. The third contribution is based upon ongoing work of developing QoE based tools for video streaming services. Two QoE based tools for the assessment of multimedia services have been presented in this thesis, their main functions are to capture, analyze and report QoE metrics in real time. These QoE tools are useful for real time measurement of QoE metrics

A l'époque où l'Internet est devenu la plateforme par défaut pour offrir de la valeur ajoutée, des nouveaux fournisseurs de services multimédia ont saisi cette opportunité en définissant les services Over-The-Top (OTT). Cependant, Internet n'étant pas un réseau de distribution fiable, il nécessaire de garantir de haut niveau de Qualité d'Expérience (QoE), ainsi que les revenues des Fournisseurs de Services d'Internet (ISP) et des OTTs.Le travail présenté dans ce document va au-delà de l'état de l'art, en proposant une solution qui prend en compte cet objectif. Les principaux apports qui y sont présentés peuvent être synthétisées en quatre contributions.En premier lieu, l'inclusion des paramètres liés aux modèles d'affaires dans l'analyse de la QoE a demandé un nouveau cadre pour calculer la QoE d'un service OTT. Ce cadre est basé sur le formalisme mathématique des Machines Étendues à États Finis (EFSM), ce qui profite de deux avantages des EFSMs~: les traces des machines suivent les décisions de l'utilisateur et les variables du contexte utilisés comme indicateurs de qualité, seront utilisées ultérieurement pour computer la QoE.La deuxième contribution consiste à mettre en œuvre deux algorithmes. Le premier fait le calcul d'une forme équivalent, ayant la forme d'un arbre qui représente les traces de la machine. Le deuxième utilise les traces et fait le calcul de la QoE pour les états terminaux de chaque trace. Les deux algorithmes peuvent être utilisés comme base d'un outil de monitorage capable de prévoir la valeur de la QoE d'un utilisateur. De plus, une mise en œuvre concrète des ces deux algorithmes comme une extension de l'Outil de Monitorage de Montimage (MMT) est aussi présentée.La troisième contribution présente la validation de l'approche avec un double objectif. D'une part, l'inclusion de paramètres du modèle d'affaires est validée et on détermine leur impact sur la QoE. D'autre part, le modèle de la QoE proposé est validé par la mise en œuvre d'une plateforme d'émulation d'un service OTT qui montre des vidéos perturbés. Cette implémentation est utilisée pour obtenir des valeurs estimées par utilisateurs réels qui sont utilisés pour dériver un modèle approprié de la QoE.La dernière contribution se base sur le cadre donné et fournit un analyse statique d'un service OTT. Cette procédure est réalisé par un troisième algorithme qui calcule la quantité des configurations contenues dans le modèle. En analysant à l'avance touts les scénarios possibles qu'un utilisateur peut rencontrer, le fournisseur des services OTT peut détecter des défauts dans le modèle et le service à une stade précoce du développement / At a time when the Internet has become the de facto platform for delivering value, the new multimedia providers took advantage of this opportunity to define Over-The-Top (OTT) services. Considering that Internet is not a reliable distribution network, it is necessary to ensure high levels of Quality of Experience (QoE) and revenues for Internet Service Providers (ISP) and OTTs. The work presented in this dissertation goes beyond the state of the art by providing a solution having this goal in mind. The main contributions presented here can be summarized in four main points.First, the inclusion of business-model related parameters in the QoE analysis required a new framework for calculating the QoE of an OTT service.This framework is based on the Extended Finite States Machine (EFSM) mathematical formalism, which takes advantage of two features of the EFSMs: (1) the traces of the machines that keep track of the user's decisions and; (2) the context variables used as quality indicators, correlated later with the QoE.The second contribution is the design and the implementation of two algorithms. The first computes the $l$-equivalent, a version in the form of a tree of the model that exposes the traces of the machine. The second uses the traces and computes the QoE at the final stages of each trace. Both algorithms can be used to design a monitoring tool that can predict a user’s QoE value. In addition, a concrete implementation is given as an extension of the Montimage Monitoring Tool (MMT).The third contribution presents the validation of the approach, having two objectives in mind. On the one hand, the inclusion of business-model related parameters was validated by determining the impact of such variables on the QoE. On the other hand, the proposed QoE model is validated by the implementation of an OTT emulation platform showing disrupted videos. This implementation is used to obtain QoE values evaluated from real users, values used to derive an appropriate QoE model.The last contribution uses the framework to perform a static analysis of an OTT service. This is done by a third algorithm that computes the amount of configurations contained in the model. By analyzing in advance all the possible scenarios a user can face -- and their respective QoE, the OTT provider can detect flaws in the model and the service from the early stages of development

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