Computational inference and control of quality in multimedia services

Quality is the degree of excellence we expect of a service or a product. It is also one of the key factors that determine its value. For multimedia services, understanding the experienced quality means understanding how the delivered delity, precision and reliability correspond to the users' expectations. Yet the quality of multimedia services is inextricably linked to the underlying technology. It is developments in video recording, compression and transport as well as display technologies that enables high quality multimedia services to become ubiquitous. The constant evolution of these technologies delivers a steady increase in performance, but also a growing level of complexity. As new technologies stack on top of each other the interactions between them and their components become more intricate and obscure. In this environment optimizing the delivered quality of multimedia services becomes increasingly challenging. The factors that aect the experienced quality, or Quality of Experience (QoE), tend to have complex non-linear relationships. The subjectively perceived QoE is hard to measure directly and continuously evolves with the user's expectations. Faced with the diculty of designing an expert system for QoE management that relies on painstaking measurements and intricate heuristics, we turn to an approach based on learning or inference. The set of solutions presented in this work rely on computational intelligence techniques that do inference over the large set of signals coming from the system to deliver QoE models based on user feedback. We furthermore present solutions for inference of optimized control in systems with no guarantees for resource availability. This approach oers the opportunity to be more accurate in assessing the perceived quality, to incorporate more factors and to adapt as technology and user expectations evolve. In a similar fashion, the inferred control strategies can uncover more intricate patterns coming from the sensors and therefore implement farther-reaching decisions. Similarly to natural systems, this continuous adaptation and learning makes these systems more robust to perturbations in the environment, longer lasting accuracy and higher eciency in dealing with increased complexity. Overcoming this increasing complexity and diversity is crucial for addressing the challenges of future multimedia system. Through experiments and simulations this work demonstrates that adopting an approach of learning can improve the sub jective and objective QoE estimation, enable the implementation of ecient and scalable QoE management as well as ecient control mechanisms

Video Quality Prediction for Video over Wireless Access Networks (UMTS and WLAN)

Transmission of video content over wireless access networks (in particular, Wireless Local Area Networks (WLAN) and Third Generation Universal Mobile Telecommunication System (3G UMTS)) is growing exponentially and gaining popularity, and is predicted to expose new revenue streams for mobile network operators. However, the success of these video applications over wireless access networks very much depend on meeting the user’s Quality of Service (QoS) requirements. Thus, it is highly desirable to be able to predict and, if appropriate, to control video quality to meet user’s QoS requirements. Video quality is affected by distortions caused by the encoder and the wireless access network. The impact of these distortions is content dependent, but this feature has not been widely used in existing video quality prediction models. The main aim of the project is the development of novel and efficient models for video quality prediction in a non-intrusive way for low bitrate and resolution videos and to demonstrate their application in QoS-driven adaptation schemes for mobile video streaming applications. This led to five main contributions of the thesis as follows:(1) A thorough understanding of the relationships between video quality, wireless access network (UMTS and WLAN) parameters (e.g. packet/block loss, mean burst length and link bandwidth), encoder parameters (e.g. sender bitrate, frame rate) and content type is provided. An understanding of the relationships and interactions between them and their impact on video quality is important as it provides a basis for the development of non-intrusive video quality prediction models.(2) A new content classification method was proposed based on statistical tools as content type was found to be the most important parameter. (3) Efficient regression-based and artificial neural network-based learning models were developed for video quality prediction over WLAN and UMTS access networks. The models are light weight (can be implemented in real time monitoring), provide a measure for user perceived quality, without time consuming subjective tests. The models have potential applications in several other areas, including QoS control and optimization in network planning and content provisioning for network/service providers.(4) The applications of the proposed regression-based models were investigated in (i) optimization of content provisioning and network resource utilization and (ii) A new fuzzy sender bitrate adaptation scheme was presented at the sender side over WLAN and UMTS access networks. (5) Finally, Internet-based subjective tests that captured distortions caused by the encoder and the wireless access network for different types of contents were designed. The database of subjective results has been made available to research community as there is a lack of subjective video quality assessment databases.Partially sponsored by EU FP7 ADAMANTIUM Project (EU Contract 214751

Planning and dynamic spectrum management in heterogeneous mobile networks with QoE optimization

The radio and network planning and optimisation are continuous processes that do not end after the network has been launched. To achieve the best trade-offs, especially between quality and costs, operators make use of several coverage and capacity enhancement methods. The research from this thesis proposes methods such as the implementation of cell zooming and Relay Stations (RSs) with dynamic sleep modes and Carrier Aggregation (CA) for coverage and capacity enhancements. Initially, a survey is presented on ubiquitous mesh networks implementation scenarios and an updated characterization of requirements for services and applications is proposed. The performance targets for the key parameters, delay, delay variation, information loss and throughput have been addressed for all types of services. Furthermore, with the increased competition, mobile operator’s success does not only depend on how good the offered Quality of Service (QoS) is, but also if it meets the end user’s expectations, i.e., Quality of Experience (QoE). In this context, a model for the mapping between QoS parameters and QoE has been proposed for multimedia traffic. The planning and optimization of fixed Worldwide Interoperability for Microwave Access (WiMAX) networks with RSs in conjunction with cell zooming has been addressed. The challenging case of a propagation measurement-based scenario in the hilly region of Covilhã has been considered. A cost/revenue function has been developed by taking into account the cost of building and maintaining the infrastructure with the use of RSs. This part of the work also investigates the energy efficiency and economic implications of the use of power saving modes for RSs in conjunction with cell zooming. Assuming that the RSs can be switched-off or zoomed out to zero in periods when the traffic exchange is low, such as nights and weekends, it has been shown that energy consumption may be reduced whereas cellular coverage and capacity, as well as economic performance may be improved. An integrated Common Radio Resource Management (iCRRM) entity is proposed that implements inter-band CA by performing scheduling between two Long Term Evolution – Advanced (LTE-A) Component Carriers (CCs). Considering the bandwidths available in Portugal, the 800 MHz and 2.6 GHz CCs have been considered whilst mobile video traffic is addressed. Through extensive simulations it has been found that the proposed multi-band schedulers overcome the capacity of LTE systems without CA. Result shown a clear improvement of the QoS, QoE and economic trade-off with CA

LTE Optimization and Resource Management in Wireless Heterogeneous Networks

Mobile communication technology is evolving with a great pace. The development of the Long Term Evolution (LTE) mobile system by 3GPP is one of the milestones in this direction. This work highlights a few areas in the LTE radio access network where the proposed innovative mechanisms can substantially improve overall LTE system performance. In order to further extend the capacity of LTE networks, an integration with the non-3GPP networks (e.g., WLAN, WiMAX etc.) is also proposed in this work. Moreover, it is discussed how bandwidth resources should be managed in such heterogeneous networks. The work has purposed a comprehensive system architecture as an overlay of the 3GPP defined SAE architecture, effective resource management mechanisms as well as a Linear Programming based analytical solution for the optimal network resource allocation problem. In addition, alternative computationally efficient heuristic based algorithms have also been designed to achieve near-optimal performance

Methods for Objective and Subjective Evaluation of Zero-Client Computing

Zero clients are hardware-based devices without a central processing unit (CPU) that deliver virtual desktops (VDs) from remote computing systems to users. We measured the performance of applications accessed through zero clients to study the feasibility of using this approach to provide a desktop-pc experience across a network. Performance evaluation is complicated because monitoring software cannot be downloaded to the zero clients. Therefore, we introduce a new methodology and metric to measure zero-client VD performance that is based on network-traffic analysis. We conducted objective and subjective studies to determine the sensitivity of application-specific metrics to different network conditions. The results show that the packet loss rate (PLR) impacts zero-client performance for some applications such as video streaming. Subjective tests showed a greater user sensitivity to the PLR for video streaming than for image viewing or Skype. A strong correlation was found between the objective and subjective measurements but the rate at which these measurements changed with increasing PLR differed depending on the application.NSF [CNS-1737453]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

A survey on Quality of Experience of Virtual and Augmented Reality environments

Οι εφαρμογές εικονικής και επαυξημένης πραγματικότητας αποτελούν μια νέα και πολλά υποσχόμενη τεχνολογία, με εφαρμογές στην ιατρική, την εκπαίδευση, τα βιντεοπαιχνίδια, το ηλεκτρονικό εμπόριο και πολλές άλλες. Αυτή η τεχνολογία θέτει μια νέα πρόκληση για τους σχεδιαστές εφαρμογών, καθώς και για τους παρόχους υπηρεσιών δικτύου, επειδή είναι εντατική ως προς τους απαιτούμενους πόρους ώστε να είναι σε θέση να παρέχει συναρπαστική εμπειρία στους χρήστες της. Αυτή η πρόκληση γίνεται ακόμη πιο δύσκολη στα δίκτυα κινητής τηλεφωνίας, λόγω παραγόντων που είναι δύσκολο να μοντελοποιηθούν και να προβλεφθούν, όπως η κινητικότητα, η στρατηγική μεταβίβασης και η κατανομή πόρων. Αυτή η διπλωματική φιλοδοξεί να παράσχει μια ανασκόπηση των τεχνικών και μεθόδων εκτίμησης της ποιότητας της εμπειρίας (QoE) και των μεθόδων που έχουν αναπτυχθεί γύρω από αυτές τις εφαρμογές. Στην πρώτη ενότητα, εξετάζουμε τις στρατηγικές παροχής QoE για εφαρμογές εικονικής πραγματικότητας. Αυτή η ενότητα εξετάζει αρκετές περιπτώσεις εφαρμογών εικονικής πραγματικότητας, όπως ένα λογισμικό προσομοίωσης βαρέων μηχανημάτων, μια εκπαιδευτική εφαρμογή και άλλες ψηφιακές εφαρμογές εμβύθισης. Το εύρος και η ποικιλία των εφαρμογών και των μεθόδων εκτίμησης της ποιότητας της εμπειρίας οδηγούν σε αντικρουόμενα συμπεράσματα σχετικά με τις μεθόδους αξιολόγησης QoE. Στην επόμενη ενότητα αναφερόμαστε σε εφαρμογές επαυξημένης πραγματικότητας, και πάλι με μια αναφορά σε μια μεγάλη ποικιλία εφαρμογών, όπως ένας βοηθός επαυξημένης πραγματικότητας, βιντεοπαιχνίδια επαυξημένης πραγματικότητας και άλλες ψηφιακές εφαρμογές. Τα συμπεράσματα σε αυτήν την ενότητα είναι πιο ισχυρά και τα συναισθήματα των ανθρώπων μπορούν να σχηματίσουν πιο ουσιαστικά πορίσματα. Στην τελευταία ενότητα διερευνούμε την QoE σε εφαρμογές εικονικής και επαυξημένης πραγματικότητας για κινητές συσκευές και κινητά δίκτυα. Σε αυτό το μέρος ασχολούμαστε με πιο τεχνικές πτυχές όπως η διαχείριση της κινητικότητας, οι στρατηγικές μεταβίβασης και οι αλγόριθμοι κατανομής πόρων και ο αντίκτυπος που έχουν αυτοί στην εμπειρία των χρηστών.Virtual and augmented reality applications constitute a new and promising technology, with applications in medicine, education, gaming, e-commerce and many more. This technology poses a new challenge to application designers, as well as network service providers, because it is resource intensive in order to be able to provide the immersive experience to its users. This task becomes even more challenging in mobile networks, due to factors that are difficult to be modeled and predicted, such as mobility, handoff strategy and resource allocation. This thesis aspires to provide a review of Quality of Experience (QoE) estimation and provision techniques and methods that have been developed around these applications. In the first section, we review QoE provisioning strategies for virtual reality applications. This section examines some corner cases of augmented reality applications, such as a heavy machinery simulation software, an educational application, and many digital immersive applications. The scope and diversity of applications and implementation methods lead to some conflicting conclusions in relation to QoE evaluation methods. In the next section we refer to augmented reality applications, again with a reference to a wide variety of applications such as an augmented reality task assistant, augmented reality video games and digital immersive applications. The conclusions in this section are more robust and peoples’ feelings can form more meaningful aggregations. In the last section we investigate the QoE in virtual and augmented reality applications when these applications are implemented in mobile devices. Τhis part is concerned with more technical aspects such as mobility management, handoff strategies and resource allocation algorithms and their impact on users’ experience

Quality-Oriented Mobility Management for Multimedia Content Delivery to Mobile Users

The heterogeneous wireless networking environment determined by the latest developments in wireless access technologies promises a high level of communication resources for mobile computational devices. Although the communication resources provided, especially referring to bandwidth, enable multimedia streaming to mobile users, maintaining a high user perceived quality is still a challenging task. The main factors which affect quality in multimedia streaming over wireless networks are mainly the error-prone nature of the wireless channels and the user mobility. These factors determine a high level of dynamics of wireless communication resources, namely variations in throughput and packet loss as well as network availability and delays in delivering the data packets. Under these conditions maintaining a high level of quality, as perceived by the user, requires a quality oriented mobility management scheme. Consequently we propose the Smooth Adaptive Soft-Handover Algorithm, a novel quality oriented handover management scheme which unlike other similar solutions, smoothly transfer the data traffic from one network to another using multiple simultaneous connections. To estimate the capacity of each connection the novel Quality of Multimedia Streaming (QMS) metric is proposed. The QMS metric aims at offering maximum flexibility and efficiency allowing the applications to fine tune the behavior of the handover algorithm. The current simulation-based performance evaluation clearly shows the better performance of the proposed Smooth Adaptive Soft-Handover Algorithm as compared with other handover solutions. The evaluation was performed in various scenarios including multiple mobile hosts performing handover simultaneously, wireless networks with variable overlapping areas, and various network congestion levels

Indicator Of Experince For Mobile Data Networks

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2013Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2013Bu çalışmada mobil veri şebekeleri için kullanıcıların yaşadıkları deneyim kalitesini ölçmek adına farklı bir metod önerilmektedir. Literatürde önerilen yöntemler ağırlıklı olarak kullanıcıların deneyimlerine ilişkin görüşlerinin anketler düzenlenerek alınması veya mobil cihazlara yerleştirilen akıllı yazılımlar ile deneyimlerin ölçülmesi şeklindedir. Bu sebeple yapılan çalışmalar örnek küme üzerinden deneyimin bütüne aitmiş gibi konumlandırılması esasına dayanmaktadır. Bu çalışmada bahsedilen yöntemlerden farklı olarak mobil şebekede bulunan sistemsel istatistiklerin kullanılması ve radyo şebekesinin deneyime etkisini hesaplayan bir modelin önerilmesiyle farklı bir hesaplama yöntemi önerilmektedir. Bu sayede sadece belli bir örnek küme değil, servisi deneyimleyen tüm kullanıcılar için bir sonuç elde etmek mümkün olmaktadır. Bu amaçla IoE kısaltması adı altında mobil veri servisleri kullanımlarında deneyimin başarısı ölçülebilir ve hesaplanabilir bir parametre ile ifade edilmiştir.In this study, introducing a new way of quality of experience calculation methodology is aimed. A new parameter, indicator of experience (IoE) has been introduced, which is a combination of quantised throughput and acceptability of the user for mobile internet service. Most QoE calculations use mobile client or user perception researches. Differently, network level statistics and radio network modeling is used to calculate IoE. This new approach is then compared with mobile client measurements in order to prove the usability of the approach.Yüksek LisansM.Sc