Quality Of Experience Factors Research Articles

A Moving Metaverse: QoE challenges and standards requirements for immersive media consumption in autonomous vehicles

The evolution of Autonomous Vehicles (AVs) has blurred the distinction between drivers and passengers, resulting in increased demand for in-car entertainment. Simultaneously, social networking platforms are evolving to adopt AR (Augmented Reality)/VR (Virtual Reality)-centric metaverses. Self-driving cars will free up the drivers and passengers to engage in leisure activities such as watching VR videos and playing games while travelling. Before driverless cars can become a “third living space”, open challenges confront the provision of immersive content (e.g., VR videos and gaming) with a high level of Quality of Experience (QoE). Current autonomous vehicular network technologies focus on navigation and safety and rely on ubiquitous high-speed 5G/6G network connectivity to provide a seamless streaming experience in AVs. The compounded risks of cyber- and motion-sickness are identified as barriers to the enjoyment of in-car VR entertainment technologies. The influence of AV car networks in different traffic conditions is identified as a key factor to minimize a person’s risk of sickness and increase overall QoE. However, assessing the QoE of immersive media consumption in AV to its full potential is a difficult task that requires proper standard guidelines and directions. Existing surveys focus only on a specific metaverse or transportation system. These surveys lack a comprehensive view of the entire immersive media consumption process, QoE factors and aspects, and QoE standards for assessing immersive content in vehicular networks. To address these issues, we present an overview of the research landscape in the context of QoE and network standards that highlights the need for experimental studies to inform and consolidate the methodologies and recommendations for standardization of immersive content QoE assessment in AVs. Research and user studies have not yet yielded standards or recommendations describing assessment methodologies to evaluate immersive systems, such as exist for traditional video and images. A framework is outlined to provide a comprehensive understanding of the QoE influencing factors and aspects that will assist in adapting, augmenting, and producing standards for immersive entertainment in autonomous vehicles.

The Impact of QoE Factors on the Perception of Constructs Comprising Information Quality, Usability, and Aesthetics in Mobile Web Browsing

Quality of Experience (QoE) for mobile Web browsing is a complex and multidimensional concept. So far, the research community has addressed the influence of QoE perceptual dimensions on QoE and the impact of various influence factors on QoE perceptual dimensions for mobile Web browsing. In this study, we recognize the gap and need to analyze and gain a deeper understanding of the relationship between multiple influence factors and specific constructs of QoE perceptual dimensions. Utilizing a previously collected dataset, our goal is to fill the research gap in terms of investigating the impact of specific influence factors (loading time, aesthetics, number of taps, information quality) on various constructs comprising the user perception of usability, aesthetics, and information quality in the context of mobile Web browsing. A total of 14 research questions are raised: 7 addressing constructs comprising perceived usability (simplicity, effectiveness, quickness, efficiency, comfort, ease of use, organization), 3 addressing constructs comprising perceived aesthetics (perception of mobile Web site aesthetics, pleasance, clarity, systematicness), and 4 addressing constructs comprising perceived information quality (timeliness, clarity, conciseness, structuralism/completeness/spelling-correctness). Results of posed research questions provide indications on the strength and direction of the impact of considered QoE influence factors on the perception of studied constructs. This study contributes to the research community by being the first to address the impact of chosen QoE influence factors on individual constructs comprising QoE perceptual dimensions in the domain of mobile Web browsing, thus extending the knowledge on interplays and relations in this area. Furthermore, it contributes by revealing numerous new paradigms in the context of usability, aesthetics, and quality of information, such as: (i) beautiful and easy to reach is more simple, effective, efficient, and comfortable, (ii) easy to reach is more pleasant and clear, (iii) informative is more pleasant and clear, (iv) beautifully presented information has higher perceived quality, etc. Reported findings can be utilized by various stakeholders (mobile Web site designers, content providers, device designers, network providers) for indirect management of QoE.

The Importance of Security Matters for Quality of Experience in Mobile Web Context

The rise of cyber-attacks coming up with the increased use of mobile devices and web services has initiated the growth of interest to understand the impact of Perception of Security (PoS) on Quality of Experience (QoE) in mobile web context and which factors impact PoS. This paper aims to investigate the relations among QoE, PoS, and multiple influence factors in different mobile web contexts. The obtained results have shown that PoS differently impacts QoE in considered mobile web contexts (banking, commerce, browsing, mail, and health). Also, the analyzed factors (risk, reliability and availability, 3rd party seals, privacy, security, grammar quality, aesthetics, reputation, brand name, and usability) have been assigned different weights of influence in terms of PoS in mobile web contexts. These findings open a wide range of potential research that could lead to the increase and improvement of users’ Quality of Life (QoL).

Modeling the User Experience of Watching 360° Videos with Head-Mounted Displays

Conducting user studies to quantify the Quality of Experience (QoE) of watching the increasingly more popular 360° videos in Head-Mounted Displays (HMDs) is time-consuming, tedious, and expensive. Deriving QoE models, however, is very challenging because of the diverse viewing behaviors and complex QoE features and factors. In this article, we compile a wide spectrum of QoE features and factors that may contribute to the overall QoE. We design and conduct a user study to build a dataset of the overall QoE, QoE features, and QoE factors. Using the dataset, we derive the QoE models for both the Mean Opinion Score (MOS) and Individual Score (IS), where MOS captures the aggregated QoE across all subjects, while IS captures the QoE of individual subjects. Our derived overall QoE models achieve 0.98 and 0.91 in Pearson’s Linear Correlation Coefficient (PLCC) for MOS and IS, respectively. Besides, we make several new observations on our user study results, such as (1) content factors dominate the overall QoE across all factor categories, (2) Video Multi-Method Assessment Fusion (VMAF) is the dominating factor among content factors, and (3) the perceived cybersickness is affected by human factors more among others. Our proposed user study design is useful for QoE modeling (specifically) and subjective evaluations (in general) of emerging 360° tiled video streaming to HMDs.

Hybrid‐360: An adaptive bitrate algorithm for tile‐based 360 video streaming

AbstractIn recent years, virtual reality (VR) has become a popular topic. VR videos, as one of the popular applications, can provide the user with an extraordinary and immersed video viewing experience and are gradually entering the public's vision. However, it has faced specific difficulties in the past few years. VR videos need specific collection and displaying equipment. Delivering VR videos needs extra bandwidth and storage space as they contain more information. Traditional adaptive bitrate (ABR) algorithms are mainly designed for 2D videos, and cannot directly apply to VR videos. The delay must be neglectable and there should be fewer stalls. If not, users can suffer from motion sickness. With the development of relevant technologies, those problems can be solved. Using a first‐class VR camera and a head‐mounted display, VR videos can be easily collected and displayed properly. In this work, Hybrid‐360, a novel tile‐based 360 video ABR algorithm, is proposed. Using reinforcement learning, the algorithm can make proper bitrate decisions for every tile in the VR video based on the network and client status. In addition, a system model is proposed to implement the tile‐based 360 ABR algorithm in the real world. Different from traditional 2D videos, VR videos have special characteristics, which brings the need to redesign quality of experience (QoE) model. Additional QoE factors are added into the QoE model, and the weights of every term are adjusted. Using a novel 360 video simulation platform, Hybrid‐360 is then compared with some classical ABR algorithms. It is found to be capable of balancing the video quality as well as video smoothness and fluentness. Also, the algorithm can provide users with a higher QoE.

Multidimensional modelling of quality of experience for video streaming

The increasing use of video streaming services that followed the Covid-19 pandemic has more than ever driven a rising interest among various stakeholders in service provisioning chain to understand factors influencing quality of experience (QoE). Many research activities have so far addressed different influence factors in order to understand and improve QoE when using video streaming service. However, we have recognized the requirement to address QoE as multidimensional concept, and show the relationship between QoE, perceptual dimensions, and influence factors. In this paper, we provide the multidimensional modelling and analysis of QoE for video streaming. Result analysis has shown that QoE for video streaming can be modelled by using perception of quality of video and perception of ease of use of application as predictors. The analysis of influence of individual system (i.e., resolution, coding tree unit (CTU), and constant rate factor (CRF)), context (i.e., location, lighting, and video type), and human (i.e., gender, education level, and prior experience) factors and their interactions on QoE perceptual dimensions (i.e., perception of quality and perception of ease of use) show statistically significant impacts, which means that alternations of these factors can enhance perceptual dimensions and consequently QoE for video streaming which is a final goal.

A new QoE-based prediction model for evaluating virtual education systems with COVID-19 side effects using data mining.

Today, emerging technologies such as 5G Internet of things (IoT), virtual reality and cloud-edge computing have enhanced and upgraded higher education environments in universities, colleagues and research centers. Computer-assisted learning systems with aggregating IoT applications and smart devices have improved the e-learning systems by enabling remote monitoring and screening of the behavioral aspects of teaching and education scores of students. On the other side, educational data mining has improved the higher education systems by predicting and analyzing the behavioral aspects of teaching and education scores of students. Due to an unexpected and huge increase in the number of patients during coronavirus (COVID-19) pandemic, all universities, campuses, schools, research centers, many scientific collaborations and meetings have closed and forced to initiate online teaching, e-learning and virtual meeting. Due to importance of behavioral aspects of teaching and education between lecturers and students, prediction of quality of experience (QoE) in virtual education systems is a critical issue. This paper presents a new prediction model to detect technical aspects of teaching and e-learning in virtual education systems using data mining. Association rules mining and supervised techniques are applied to detect efficient QoE factors on virtual education systems. The experimental results described that the suggested prediction model meets the proper accuracy, precision and recall factors for predicting the behavioral aspects of teaching and e-learning for students in virtual education systems.

Reinforcement learning-based QoE-oriented dynamic adaptive streaming framework

Dynamic adaptive streaming over the HTTP (DASH) standard has been widely adopted by many content providers for online video transmission and greatly improve the performance. Designing an efficient DASH system is challenging because of the inherent large fluctuations characterizing both encoded video sequences and network traces. In this paper, a reinforcement learning (RL)-based DASH technique that addresses user quality of experience (QoE) is constructed. The DASH adaptive bitrate (ABR) selection problem is formulated as a Markov decision process (MDP) problem. Accordingly, an RL-based solution is proposed to solve the MDP problem, in which the DASH clients act as the RL agent, and the network variation constitutes the environment. The proposed user QoE is used as the reward by jointly considering the video quality and buffer status. The goal of the RL algorithm is to select a suitable video quality level for each video segment to maximize the total reward. Then, the proposed RL-based ABR algorithm is embedded in the QoE-oriented DASH framework. Experimental results show that the proposed RL-based ABR algorithm outperforms state-of-the-art schemes in terms of both temporal and visual QoE factors by a noticeable margin while guaranteeing application-level fairness when multiple clients share a bottlenecked network.

QoE-Aware Intelligent Satellite Constellation Design in Satellite Internet of Things

In Satellite Internet of Things (SIoT), satellite constellation design is used to satisfy more communication demands with fewer satellites by optimizing satellite orbits in specific areas. However, the diversity of user demands and satellite resource poses a great challenge in evaluating the Quality of Experience (QoE) of the satellite constellation. In this article, a QoE-aware satellite constellation design scheme is proposed to enhance the user satisfaction by constructing QoE factors. First, an SIoT network model is established with low-Earth-orbit (LEO) satellites and ground Internet of Things (IoT) devices, and the problem is formulated to calculate the QoE for different areas after analyzing the dynamic network topology. Then, the QoE factors are defined to assess QoE by taking into account the coverage performance, communication fluency, regional demand capacity, and profitability. Following that, an intelligent optimization algorithm named multilayer tabu search (MLTS) is designed to obtain reasonable satellite orbits with the best QoE, which provides orbit parameters for satellite constellation configuration. The simulation results demonstrate that the designed satellite constellation can effectively improve the users QoE by optimizing the various QoE factors.

QoE-Constrained Concurrent Request Optimization Through Collaboration of Edge Servers

Cloud computing, which is claimed to provide plentiful storage, computational, and other resources, has become a promising platform to support resource-intensive applications. Due to the wide adoption of smart things to support domain applications and considering the delay-sensitivity of certain requests and limited network capacity compared with huge data packets to be transmitted, the quality of experience (QoE) may be hard to be satisfied when requests are solely supported by cloud computing. In this setting, edge computing has become an infrastructure to facilitate request satisfaction at the network edge. This article proposes a mechanism to optimize the collaboration of heterogeneous edge servers with certain QoE constraints. Specifically, concurrent requests, which are usually represented in terms of SQL queries, are rewritten as atomic queries, and these atomic queries are optimally assigned to edge servers through adopting an algorithm inspired by the minimum spanning tree, where QoE factors, including the delay, size of data packets, and number of operators, are considered. Evaluation results indicate that the proposed mechanism can effectively improve the QoE of requests compared with the state-of-the-art’s mechanisms.

Using 360 VR video to improve the learning experience in veterinary medicine university degree

We present the results of the Quality of Experience (QoE) evaluation of 360 degree immersive video in university education. Fourth-year Veterinary Medicine students virtually attended some practical lessons which had been recorded in immersive 360 video format, covering topics of Surgical Pathology and Surgery related to horses. One hundred students participated in the experience. They evaluated it through an extensive questionnaire covering several QoE factors, including presence, audiovisual quality, satisfaction or cybersickness: 79% evaluated the experience as excellent or good, and they acknowledged an improvement of the learning process by the implementation of VR as didactic tool, and 91% reported that they would recommend it to other students. Female students consistently gave slightly better average scores than their male counterparts, although mostly within confidence intervals. Strongest inter-gender differences appeared in active social presence dimensions, according to the Temple Presence Inventory. The study also evaluates the suitability of synthetic measurement protocols, such as the Distributed Reality Experience Questionnaire (DREQ) and Net Promoter Score (NPS). We show that NPS is a valid tool for QoE analysis, but that its clustering boundary values must be adapted to the specificities of the experiment population.

문화유산 관람자의 공간 스케줄링 최적화에 의한 경험만족도(QoE) 제고 연구

Thanks to the advances in artificial intelligence and media technology, heritage experience can be digitally augmented and highly personalized for each single user. It is essential to customize visitor’s experience by interweaving the sequence of spatial experience to ensure the successful guided heritage tour. Intelligent routing algorithm is used for this purpose. However, the quality of experience(QoE) deteriorates due to the inefficient route and the congestion in the exhibition space. The purpose of this study is to optimize the visitor’s QoE according to the route for architectural heritage generated from the tour guide system. For this purpose, a model optimizing the visitor’s QoE was developed. In the QoE optimization model, 1) a schedule indicating the visitor’s location of every moment according to the generated route is created, 2) QoE factors such as the congestion degree, the total length of the route, and the number using stairs are measured through the indoor spatial information described in IndoorGML and 3) the QoE optimization is performed using the genetic algorithm. In order to verify the QoE optimization model, it was applied to the case of Seokjojeon, a cultural heritage of modern architecture. The result of QoE optimization for 10 visitors shows that the congestion degree is enhanced by 50%, the total length of the route and the number using stairs are improved by 12% and 13% compared to the state before the optimization.

QoE-Driven Joint Optimization of Segment Adaptation and Resource Allocation for DASH Clients Over LTE Networks

In this paper, we focus on a comprehensive dynamic adaptive streaming over HTTP (DASH) framework, which consists of video content analysis at the HTTP adaptive streaming server, joint optimization of segment switch and resource allocation at eNodeB, and playback information extraction at the client, to improve the quality of experience (QoE) of the DASH clients over long-term evolution networks. We explore the unique characteristics of each segment by establishing the size-time and size-distortion models based on the video packet information extracted from the encoder rather than modeling the entire video sequence by a fixed function. Video quality, stability, and playback continuity can be predicted and reflected by these constructed models. Considering the above three factors, we develop an accumulative QoE model by calculating and predicting the QoE factors of each segment. Then, the entire framework is formulated as a mixed-integer non-linear programming mathematical model to maximize the total QoE of the DASH clients, where decisions on resource allocation and video segment switch are jointly optimized in a semi-synchronous centralized mode. Resource allocation consists of resource block assignment and modulation and coding scheme selection rather than only determining the resource utilization percentage. To perform the optimization, we develop a low-complexity heuristic algorithm that decomposes the original problem into multiple subproblems. The simulation results show that our proposed algorithms can efficiently improve the QoE level compared with other existing algorithms.

A quantitative relationship between Application Performance Metrics and Quality of Experience for Over-The-Top video

Quality of Experience (QoE) is a measure of the overall level of customer satisfaction with a vendor. In telecommunications, consumer satisfaction is of great interest in the adoption of novel multimedia products and services. A number of factors can greatly influence the customer experience during a video session. Factors such as user perception, experience, and expectations are expressed by QoE while factors such as application and network performance are expressed by Quality of Service (QoS) parameters. This paper studies the relationship between QoS and QoE in a session-based mobile video streaming. Specific QoS Application Performance Metrics (APMs) are examined based on a QoE assessment database which is built for experimentation and contains 108 subjects. It is shown that these APMs are highly related to two QoE factors, Technical Quality (TQ) and Acceptability. Furthermore, Viewing Ration (VR) parameter and the corresponding Kendall correlation between VR and QoE factors proves that VR is a valuable metric for mapping QoS to QoE. We further generated the compacted decision tree to predict QoE factors through Rebuffering Ratio (RR), Non-interruption Content Viewing Ratio (VRc), and Non-interruption Viewing Ratio (VRs). Through extensive experimentation, a general relationship between APMs and QoE factors has been examined and a primary QoE model is proposed based on this relationship.

Can You See What I See? Quality-of-Experience Measurements of Mobile Live Video Broadcasting

Broadcasting live video directly from mobile devices is rapidly gaining popularity with applications like Periscope and Facebook Live. The quality of experience (QoE) provided by these services comprises many factors, such as quality of transmitted video, video playback stalling, end-to-end latency, and impact on battery life, and they are not yet well understood. In this article, we examine mainly the Periscope service through a comprehensive measurement study and compare it in some aspects to Facebook Live. We shed light on the usage of Periscope through analysis of crawled data and then investigate the aforementioned QoE factors through statistical analyses as well as controlled small-scale measurements using a couple of different smartphones and both versions, Android and iOS, of the two applications. We report a number of findings including the discrepancy in latency between the two most commonly used protocols, RTMP and HLS, surprising surges in bandwidth demand caused by the Periscope app’s chat feature, substantial variations in video quality, poor adaptation of video bitrate to available upstream bandwidth at the video broadcaster side, and significant power consumption caused by the applications.

Developing a Quality of Experience (QoE) model for Web Applications

AbstractWeb‐based service providers have long been required to deliver high quality services in accordance with standards and customer requirements. Increasingly, however, providers are required to think beyond service quality and develop a deeper understanding of their customers' Quality of Experience (QoE). Although models exist that assess the QoE of Web Application, significant challenges remain in: (1) Defining QoE factors from a Web engineering perspective, (2) quantifying the relationship between so‐called “objective” and “subjective” factors of relevance, and (3) dealing with limited data available in relation to subjective factors. In response, the work here presents a novel model (and associated software instantiation) that integrates factors through Key Performance Indicators and Key Quality Indicators. The mapping is incorporated into a correlation model that assesses the QoE of Web Applications, with a consideration of defining the factors in term of quality requirements derived from web architecture. The data resulting from the mapping is used as input of the proposed model to develop artefacts that quantify and predict QoE using Machine Learning. The development of proposed model is framed and guided by Design Science Research approach with the purpose of enabling providers to more informed decisions regarding QoE and/or to optimise resources accordingly. Although the work is oriented at developing an artefact that has clear utility for practice, the nascent design theory underpinning the work is developed and discussed.

QoE-Guided Warping for Stereoscopic Image Retargeting.

In the field of stereoscopic 3D (S3D) display, it is an interesting as well as meaningful issue to retarget the stereoscopic images to the target resolution, while the existing stereoscopic image retargeting methods do not fully take user's Quality of Experience (QoE) into account. In this paper, we have presented a QoE-guided warping method for stereoscopic image retargeting, which retarget the stereoscopic image and adapt its depth range to the target display while promoting user's QoE. Our method takes shape preservation, visual comfort preservation, and depth perception preservation energies into account, and simultaneously optimizes the 2D coordinates and depth information in 3D space. It also considers the specific viewing configuration in the visual comfort and depth perception preservation energy constraints. Experimental results on visually uncomfortable and comfortable stereoscopic images demonstrate that in comparison with the existing stereoscopic image retargeting methods, the proposed method can achieve a reasonable performance optimization among the QoE's factors of image quality, visual comfort, and depth perception, leading to promising overall S3D experience.

Quality of experience evaluation of HTTP video streaming based on user interactive behaviors

User interactive behaviors play a dual role during the hypertext transfer protocol (HTTP) video service: reflection and influence. However, they are seldom taken into account in practices. To this end, this paper puts forward the user interactive behaviors, as subjective factors of quality of experience (QoE) from viewer level, to structure a comprehensive multilayer evaluation model based on classic network quality of service (QoS) and application QoS. First, dual roles of user behaviors are studied and the characteristics are extracted where the user experience is correlated with user interactive behaviors. Furthermore, we categorize QoE factors into three dimensions and build the metric system. Then we perform the subjective tests and investigate the relationships among network path quality, user behaviors, and QoE. Ultimately, we employ the back propagation neural network (BPNN) to validate our analysis and model. Through the simulation experiment of mathematical and BPNN, the dual effects of user interaction behaviors on the reflection and influence of QoE in the video stream are analyzed, and the QoE metric system and evaluation model are established.

An investigation into the relationship between perceived quality-of-experience and virtual acoustic environments : the case of 3D audio telephony

Quality of Experience (QoE) is a human centric quality evaluation method which provides the blue print of human needs, perceptions, feelings and experiences with respect to a multimedia service. In a communications ecosystem, human interaction takes place alongside technological, contextual, and business domains, thus producing a holistic view on QoE formation. In this paper, we investigate the relationship between human perceived QoE and context for burgeoning 3-dimensional (3D) audio teleconferencing services. 3D audio teleconferencing applications are customizable by generating different virtual acoustic environments (VAE), where parameters such as virtual room size and competing talker conditions can be adjusted for a particular application. The impact of different VAE characteristics on perceived QoE, however, is still unknown. In this study, four QoE factors were investigated across different VAE scenarios. It was found that a) medium-size virtual rooms produce optimal perceived QoE, b) competing talkers of mixed gender could be easily located in the virtual space, and c) competing speaker gender had no significant effect on perceived audio quality

Quality user experience in advanced IP video services

The continuous improvement in the delivery of advanced video services, along with evolved technical conditions at the client side, contributed to the appearance of new methods for the evaluation of quality of service (QoS) and quality of experience (QoE) from the user’s point of view. This article describes the development of a model that correlates QoS parameters and QoE factors with impact on the variation of the user’s perception of the quality. A quality assessment test was performed with 40 participants that ranked more than 140 videos. The detailed analysis of the collected data from the test allowed to conclude that all the considered factors had significant impact on the perceived quality. Those factors were aggregated in a single model using linear regression techniques to join their behavior and associate adequate weights to each factor. The results from validity tests of the model were encouraging, achieving 99 % of accuracy. This model can be considered a new no-reference metric to infer the perceived quality, applicable in many contexts, but essentially as a tool for service providers to estimate the rank customers may give to a content.