QoE Research Papers - Academia.edu (original) (raw)

Video quality estimation is crucial for the efficient management of video delivery services. Particularly with the advances in screen technology and content delivery networks, getting an accurate estimation of the video quality as it is actually perceived by the user, is a key factor in delivering high quality-of-experience. Psychometric scaling provides the tools to measure the impact that different types of impairments have on the delivered quality. In contrast to the more conventional subjective rating procedures, psychometric scaling does not suffer from biases and has significantly lower variability. However, the existing psychometric methods such as Maximum Likelihood Difference Scaling (MLDS) entail a large number subjective tests. Herein we present an adaptive approach that leads to improvement in the learning rate and, in turn, to resource-efficient video delivery systems.

- by
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- Quality of Experience, QoE, Maximum Likelihood Difference Scaling, adaptive MLDS

Cloud gaming is an emerging technology that combines cloud computing with computer games. Compared to traditional gaming, its core advantages include ease of development/deployment for developers, and lower technology costs for users given the potential to play on thin client devices. In this chapter, we firstly describe the approach, and then focus on the impact of latency, known as lag, on Quality of Experience, for so-called First Person Shooter games. We outline our approach to lag compensation whereby we equalize within reason the up and downlink delays in real-time for all players. We describe the testbed in detail, the open source Gaming Anywhere platform, the use of NTP to synchronise time, the network emulator and the role of the centralized log server. We then present results that firstly validate the mechanism and also use small scale and preliminary subjective tests to assess and prove its performance. We conclude the chapter by outlining ongoing and future work.

Quality of Service (QoS) and Quality of Experience (QoE) are considered key performance indicators of video and audio conference performance of video call streaming service. Since Skype operates and evolves around the scope of achieving best possible quality service across all available operative environments, it is important to investigate and examine how Skype has evolved around this cross platform scope, what kind of encoding improvements have been implemented through years and how efficient are all these changes, objectively and subjectively.

This paper investigates the impact of different audio codecs typically deployed in current digital audio broadcasting (DAB) systems and web-casting applications, which represent a main source of quality impairment in these systems and applications, on the quality perceived by the end user. Both subjective and objective assessments are used. Two different audio quality prediction models, namely Perceptual Evaluation of Audio Quality (PEAQ) and Perceptual Objective Listening Quality Assessment (POLQA) Music, are evaluated by comparing the predictions with subjectively obtained grades. The results show that the degradations introduced by the typical lossy audio codecs deployed in current DAB systems and web-casting applications operating at the lowest bit rate typically used in these distribution systems and applications seriously impact the subjective audio quality perceived by the end user. Furthermore, it is shown that a retrained POLQA Music provides the best overall correlations between predicted objective measurements and subjective scores allowing to predict the final perceived quality with good accuracy when scores are averaged over a small set of musical fragments (R = 0.95).

- by Peter Pocta and +1
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- Acoustic Engineering, Telecommunications Engineering, Acoustics, Signal Processing

This chapter discusses prospects of QoE management for future networks and applications. After motivating QoE management, it first provides an introduction to the concept by discussing its origins, key terms and giving an overview of the most relevant existing theoretical frameworks. Then, recent research on promising technical approaches to QoE-driven management that operate across different layers of the networking stack is discussed. Finally, the chapter provides conclusions and an outlook on the future of QoE management with a focus on those key enablers (including cooperation, business models and key technologies) that are essential for ultimately turning QoE-aware network and application management into reality.

—This paper deals with a video content classification based on spatial and temporal information. The main goal of the paper is to find out whether the non-expert test subjects are able to correctly recognize the spatial and temporal information and how precise is the corresponding parameterization of video content based on these two attributes. Moreover, how well do the objective values provided by the tool deployed in this experiment correlate with the subjective ones. The subjective measurement has shown that the non-expert test subjects are able to correctly recognize the spatial and temporal information only for the extreme scenes in terms of the spatial and temporal information but have experienced some problems when it comes to a more granular classification. When it comes to the objective assessment, the SI and TI values provided by the tool used in this experiment correlate quite well with the subjective data obtained from the basic test for the spatial information.

- by Peter Pocta
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- Telecommunications Engineering, Spatial Analysis, Telecommunications, Service Quality

This paper presents a brief review of some existing correlation models which attempt to map Quality of
Service (QoS) to Quality of Experience (QoE) for multimedia services. The term QoS refers to deterministic
network behaviour, so that data can be transported with a minimum of packet loss, delay and maximum
bandwidth. QoE is a subjective measure that involves human dimensions; it ties together user perception,
expectations, and experience of the application and network performance. The Holy Grail of subjective
measurement is to predict it from the objective measurements; in other words predict QoE from a given set
of QoS parameters or vice versa. Whilst there are many quality models for multimedia, most of them are
only partial solutions to predicting QoE from a given QoS. This contribution analyses a number of previous
attempts and optimisation techniquesthat can reliably compute the weighting coefficients for the QoS/QoE
mapping

Dynamic Adaptive Streaming over HTTP (DASH) enhances the Quality of Experience (QoE) for users by automatically switching quality levels according to network conditions. Various adaptation schemes have been proposed to select the most suitable quality level during video playback. Adaptation schemes are currently based on the measured TCP throughput received by the video player. Although video buffer can mitigate throughput fluctuations, it does not take into account the effect of the transition of quality levels on the QoE. In this paper, we propose a QoE-aware DASH system (or QDASH) to improve the user-perceived quality of video watching. We integrate available bandwidth measurement into the video data probes with a measurement proxy architecture. We have found that our available bandwidth measurement method facilitates the selection of video quality levels. Moreover, we assess the QoE of the quality transitions by carrying out subjective experiments. Our results show that users prefer a gradual quality change between the best and worst quality levels, instead of an abrupt switching. Hence, we propose a QoE-aware quality adaptation algorithm for DASH based on our findings. Finally, we integrate both network measurement and the QoE-aware quality adaptation into a comprehensive DASH system.

- by Ricky K. P. Mok
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- QoS and QoE, QoS, QoE, Network Monitoring and Measurements

—Recently, we have seen an emergence of affordable Head Mounted Displays (HMD) such as the Oculus Rift, HTC Vive, and the PS4 Project Morpheus which allow users to experience 3D virtual reality (VR). These types of hardware aim to facilitate new and novel experiences for users above and beyond what is possible with traditional audiovisual displays. However, a very limited number of studies exist in the literature to determine the influence of these technologies on user Quality of Experience (QoE). In order to evaluate QoE as users consume VR content, this paper proposes the use of affordable consumer electronics to capture objective physiological metrics: Heart Rate (HR) and ElectroDermal Activity (EDA). Our findings indicate different HR and EDA dependent on VR and non-VR environments. Additionally, we examine the relationship between these objective metrics and user QoE captured via a post-test questionnaire. To the best of the authors knowledge, this is the first work which demonstrates a tangible relationship between the EDA/HR combination and user QoE of immersive VR environments.

- by Niall Murray
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- User Experience (UX), Virtual Reality, QoE, Quality of Experience (QoE)

Whereas some application domains show a certain consensus on the role of system factors, human factors, and context factors, QoE management of multimedia systems and services is still faced with the challenge of identifying the key QoE influence factors. In this chapter, we focus on the potential of enhancing QoE management mechanisms by exploiting valuable context information. To get a good grip on the basics we first discuss a general framework for context monitoring and define context information, including technical, usage, social, economic, temporal, and physical factors. We then iterate the opportunities and challenges in involving context in QoE monitoring solutions, as context may be, e.g., hard to ascertain or very situational.

- by Peter Pocta and +2
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- Telecommunications Engineering, Computer Science, Human Computer Interaction, Computer Engineering
- by Antonio Sanchez
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- QoS and QoE, QoS, QoE, On

This article proposes a framework to design and implement e- Health interventions in a comprehensive manner. We draw on complexity science to study the interplay of the ecosystem, the behavior and interactions among its agents. We provide a platform to estimate the Quality of Experience (QoE) to assess the relationship between technology and human factors involved in e-Health projects. Our aim is to estimate QoE in e-Health ecosystems from the perspective of complexity by adopting a methodology that uses fuzzy logic to study the behavior of the ecosystem’s agents. We apply the proposed framework to a remote diagnosis case by means of an ultrasound probe through a satellite link. Despite the ambiguities for determining QoE, the experiment demonstrates the applicability of the framework and allows to stressing the importance of human factors in the implementation of e-Health projects.

- by Salvador Reyes
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- Engineering, Fuzzy Logic, E Health, QoS and QoE
- by Khalil Laghari
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- Neuroimaging, NIRS, EEG, QoE

There is a general trend towards the decentralization
of mobile operator networks. Such network decentralization
will not be efficient without rethinking mobility management
schemes, particularly for users moving for a long distance and/or at a high speed (e.g., vehicles). To support such highly mobile users, this paper introduces a data anchor gateway relocation method based on user mobility, history information, and user activity patterns. The performance of the proposed schemes is evaluated through simulations and encouraging results are obtained.

- by Konstantinos Samdanis and +1
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- Wireless Communications, Virtualization, Mobile Computing, Cloud Computing
- by Vlado Menkovski
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- Quality of Experience, Video Streaming, QoE, Video quality assessment

—With the aim to enrich users' perceived multimedia experience, the authors present the results of an empirical study which looked at user perception of olfaction based mulsemedia. The goal is to evaluate the influence of users' age and gender on user quality of experience (QoE) considering various scent types and categories (pleasant or not). The results present a complex relationship between these variables and how they influence user QoE. They indicate that different user groups report different perception of content level factors for olfaction based mulsemedia.

- by Niall Murray and +2
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- Multimedia, Multimedia Communications, Olfaction, QoE

Integration of mobile devices in the m-learning systems provides the learner with scaffolding outside the classroom, it allows them to easily store, record and deliver multimedia content in real-time. The process of delivering multimedia learning objects (rich text, video, images, audio, animation, etc.) from the mlearning systems to the users requires more computational resources than mobile devices can provide.
Considering the existence of different kinds of mobile browsers, which have limited support for HTML plugins (Flash, Java and Silverlight), we face the challenge to deliver the adapted multimedia contents in the interactive m-learning system. Installation of add-ons to the mobile browsers is just a provisional solution that is not always possible on each mobile device. Generally, the m-learning systems presented on
the mobile device that not provide rich multimedia information leads to a degraded learning experience. In order to provide users with multimedia content that is suitable for their mobile devices and according to their needs we introduce the mobile cloud computing environment as paradigm that is ideal to overcome these problems. The proposed interactive mCloud system should provide high scale collaboration and interaction between the professor and students, in direction of increasing the quality of learning. The main focus is the delivery
of multimedia learning objects to the users depending on the student’s cognitive style and adapting the content in accordance with the context-aware network conditions. The main task within this paper is to design a QoE model for estimating the multidimensional metric based on multimedia content adaptive features.

- by Manuela Pereira
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- Information Systems, Computer Science, Signal Processing, Multimedia

Existing mobile networking systems lack the level of intelligence, scalability, and autonomous adaptability required to optimally enable next-generation networks like 5G and beyond, which are expected to be Self Organizing Networks (SONs). It is anticipated that machine learning (ML) will be instrumental in designing future “x”G SON networks with their demanding Quality of Experience (QoE) requirements. This paper evaluates a methodology that uses supervised machine learning to predict the QoE level of the end user experiences and uses this information to detect anomalous behavior of dysfunctional network nodes (eNodeBs/base stations) in self-organizing mobile networks. An end-to-end network scenario is created using the network simulator ns-3, where end users interact with a remote host that is accessed over the Internet to run the most commonly used applications like file downloads and uploads and the resulting output is used as a dataset to implement ML algorithms for QoE prediction and eNodeB (eNB) anomaly detection. Three ML algorithms were implemented and compared to study their effectiveness and the scalability of the methodology. In the test network, an accuracy score greater than 99% is achieved using the ML algorithms. As suggested by the ns-3 simulation the use of ML for QoE prediction will help network operators understand end-user needs and identify network elements that are failing and need attention and recovery.

- by Karan Mitra
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- Prediction, Passive, Active, Learning

Real-time communication (RTC) applications like VoIP ideally require networks that support the necessary quality of service (QoS) whereas the reality is that network impairments such as latency, jitter and packet loss exist. In order to cope with jitter and delay, some VoIP applications employ timescale modification or warping in the jitter buffer that adjusts the rate of playout while controlling the pitch to minimize Mouth-to-Ear (M2E) delay whilst preserving speech intelligibility and quality. In this paper, we firstly investigate the extent to which time-scaling occurs using WebRTC [1] VoIP clients over Wi-Fi networks with different levels of congestion. We then assess the impact of such time-scaling, both subjectively via expert listening test and objectively using POLQA, on quality experienced by the end user, and review the correlation between scores.

- by Peter Pocta and +1
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- Telecommunications Engineering, Computer Science, Human Computer Interaction, Communication

In this paper, we propose a new method for assessing
the sociability scalar of a mobile peer by the network,
most importantly with no involvement of the mobile peer. The
sociability metric can help in the Application Layer Traffic
Optimization (ALTO) guidance in Mobile Peer-to-Mobile Peer
(MP2MP) scenario to scale up the database search of an ALTO
server. The proposed method models encounters of mobile peers with predetermined areas, such as cells, tracking areas, gateway service areas, etc, depending on the targeted granularity. The obtained metrics, pertaining to inter-mobile peer relationship (i.e., sociability) and mobile peers mobility, are adopted to ALTO in a MP2MP scenario. In addition, metrics reflecting the energy budget of a mobile peer, the type of a mobile terminal, history of a mobile terminal in sharing contents with other mobile peers, etc, can be also taken into account by ALTO in the peer recommendation.

- by Tarik Taleb and +1
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- Wireless Communications, Virtualization, Mobile Computing, Cloud Computing

Quality-of-Experience (QoE) is a human centric notion that produces the blue print of human perception, feelings, needs and intentions while Quality-of-Service (QoS) is a technology centric metric used to assess the performance of a multimedia application and/or network. To ensure superior video QoE, it is important to understand the relationship between QoE and QoS. To achieve this goal, we conducted a pilot subjective user study simulating a video streaming service over a broadband network with varying distortion scenarios, namely packet losses (0, 0.5, 1, 3,7, and 15%), packet reorder (0, 1, 5, 10, 20, and 30%), and coding bit rates (100, 400, 600, and 800 Kbps). Users were asked to rate their experience using a subjective quantitative metric (termed Perceived Video Quality, PVQ) and qualitative indicators of “experience.” Simulation results suggest a) an exponential relationship between PVQ and packet loss and between PVQ and packet reorder, and b) a logarithmic relationship between PVQ and video bit rate. Similar trends were observed with the qualitative indicators. Exploratory analysis with two objective video quality metrics suggests that trends similar to those obtained with the subjective ratings were obtained, particularly with a full-reference metric.

- by Khalil Laghari
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- Multimedia, BCI, QoE
- by Vlado Menkovski
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- Quality of Experience, QoE

Quality of Experience (QoE) is emerging as the holy grail of human-centric multimedia services. QoE is a multi-disciplinary field based on social psychology, cognitive science, economics and engineering science, focused on understanding overall human quality of experience requirements. QoE has been viewed as the pivotal set of metrics in determining the success or failure of any product or service. In real time environment, QoE is influenced by multiple service factors such as application and network level QoS ...

- by Imran Khan
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- Cognitive Science, Social Psychology, Computer Science, Qualitative methodology

The advent of 5G technology is believed to have mutual basic requirements such as high reliability and extremely high data rates. Meeting up with such demand changes the overall fifth generation (5G) network architecture. 5G is aim at interconnecting all wireless Heterogeneous Network (HetNet) for seamless and reliable Quality of Experience (QoE) where every network is in control of its UE's mobility, while the network providers manages the operation at the terminal. The proliferation of Voice over IP (VoIP) applications over the years attracts the attention of a number of telecommunication industries. VoIP is targeting to overtake the existing traditional Public Switch Telephone Network (PSTN), with the packet based network, that can support multimedia services globally. In this work we proposed a new standalone Internet Telephony Transport Protocol (ITTP) architecture that unite the functionality User Datagram Protocol (UDP) and Real-time Transmission Protocol (RTP) to convey media data aim at providing better performance in terms of bandwidth, delay and throughput based on the proposed model equations as compared to the existing inefficient protocol. The protocol provides VoIP developers with options to augment new services and technique for other use cases and future work toward implementing the architecture is also discussed.

- by Surajo Muhammad
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- QoE, VoIP over WiFi, VoIP Performance, 5g Technology
- by Khalil Laghari
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- User Experience (UX), Qualitative methodology, Rough Sets, Quantitative Methods
- by Edmond W. W. Chan and +2
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- User Experience (UX), QoS and QoE, QoE, HTTP Streaming
- by Florence Agboma
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- Distributed Computing, Quality of Experience, QoE, Multimedia Streaming
- by Khalil Laghari and +2
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- Human Computer Interaction, Neuroimaging, EEG, BCI

We present a framework that allows for simulating a cloud gaming architecture with any DirectX-based video game. The gamer’s screen is captured and compressed as a video stream using the VP8 codec and sent over the network using UDP. We use this framework to evaluate the influence of frame-rate and encoding bit-rate on the Quality of Experience, as perceived by a panel of human test subjects.

- by Yohann Pitrey
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- Quality of Experience, QoE, Cloud Gaming, VP8
- by Antonio Liotta
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- Peer-to-Peer, QoS and QoE, IPTV, QoE

Abstract: The user perceived quality or Quality of Experience (QoE) is of significant importance to multimedia service providers because of its relevance for efficient management of provided services. However, due to its subjective nature, QoE is difficult to estimate. Subjective methods are costly and impractical, while objective methods do not correlate precisely with the subjective perception. In addition to the challenges in estimating QoE, further challenges are presented in determining the means of managing the QoE in ...

- by Antonio Sánchez
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- QoS and QoE, Quality of Experience, QoE, On
- by Antonio Sanchez
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- Emerging Technologies, Quality of Experience, QoE, Multimedia Streaming
- by Antonio Sanchez
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- Emerging Technologies, Quality of Experience, QoE, Multimedia Streaming

Due to their numerous advantages, current trends
show a growing number of femtocell deployments. However,
femtocells would become less attractive to the general consumers if they cannot keep up with the service quality that the macro cellular network should provide. Given the fact that the quality of mobile services provided at femtocells depends largely on the level of congestion on the backhaul link, this paper introduces a flow mobility/handover admission control method that makes decisions on layer-three handovers from macro network to femtocell network and/or on entire or partial flow mobility between the two networks based on predicted QoS taking into account
metrics such as network load/congestion indications and based on predicted QoE metrics. The performance of the proposed admission control is evaluated via simulations and encouraging results are obtained

- by Tarik Taleb
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- Wireless Communications, Virtualization, Mobile Computing, Cloud Computing
- by R. Schatz and +1
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- QoE, Mobile Broadband Networks, Field Trial, Live Network

The advances in IPTV (Internet Protocol Television) technology enable a new user-centric and interactive TV model, in which context-awareness is promising in making the user’s interaction with the TV dynamic and transparent. Our research interest is how to achieve user-centric personalized IPTV services applying context-awareness. In this paper, we present a solution for IPTV services personalization introducing context-awareness on top of the IPTV architecture on one hand to gather different information about the user and his environment and on the other hand allowing each user to be distinguished to the system in a unique manner. Consequently, IPTV services personalization is achieved in a real-time manner for each user. We implemented the proposed solution on top of an IPTV platform considering the NGN IPTV architecture as a proof of concept and as a means to evaluate the performance. Povzetek: Članek opisuje prilagajanje televizije IP posamičnim uporabnikom z uporabo konteksta. 1

- by Hassnaa Moustafa
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- Personalization, IPTV, QoE, NGN

- by ikram khan
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- Cognitive Science, Social Psychology, Computer Science, Wireless Communications

- by Ingeniería e Investigación and +1
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- Fuzzy Logic, E-Health, QoS and QoE, Complexity Science

The Quality of Experience (QoE) is an irreplaceable metric for evaluating the perceived quality of consumers of multimedia content. Due to the subjectiveness of QoE the most suitable way to measure it is by executing subjective studies. However, executing subjective studies is a complex and expensive process. Careful recreation of the viewing conditions is necessary, and a strict selection of the test subjects is required based on many criteria. This is why solutions are often found in various objective methodologies for measuring the QoE of multimedia. These solutions even though more practical are less accurate and cannot reflect the user expectations. In this work we present a method for building QoE prediction models using machine learning techniques from continuous real-time customer feedback, i.e., during the service execution. This online learning approach builds and adapts prediction models that estimate the QoE based on given Quality of Service metrics from real-time user feedback and does not require apriori execution of subjective studies.

- by Vlado Menkovski
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- Quality of Experience, QoE, Online Learning (Machine Learning)
- by Ricky K. P. Mok and +1
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- Youtube, QoS and QoE, QoE, HTTP Streaming
- by imran khan
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- Cognitive Science, Social Psychology, Wireless Communications, User Experience (UX)