ABSTRACT
In recent years, virtual reality (VR) has gradually entered the daily education and teaching activities from pure scientific research. In the area of assistance teaching, some typical computer softwares still play some important roles. This makes remote teaching activities can just learn voice, yet cannot possess the feeling of realistic existence. Especially in scenario of COVID-19, remote teaching activities with proper perceptibility are in urgent demand. To deal with the current challenge, this paper proposes a wireless VR-based multimedia-assisted teaching system framework under mobile edge computing networks. In this framework, cooperative edge caching and adaptive streaming based on viewport prediction are adopted to jointly improve the quality of experience (QoE) of VR users. First, we investigated the resource management problem of caching and adaptive streaming in this framework. Considering the complexity of the formulated problem, a distributed learning scheme is proposed to solve the problem. The experimental data are verified and the experimental results prove that the studied methods improve the performance of user QoE. [ FROM AUTHOR] Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
In this chapter, research about the assessment of video quality for gaming content will be provided. At first, a dataset that was used for the development of the ITU-T Rec. G.1072 will be presented. The dataset was created in a laboratory environment using the passive test paradigm described in Chap. 3. Next, some results of the collected video quality ratings will be illustrated. While QoE assessment studies traditionally make use of controlled laboratory environments, there are also other possibilities to conduct user studies without a laboratory environment. Especially during the COVID-19 pandemic, which prevented many researchers from performing lab studies, the concept of supervised and unsupervised remote studies got lots of attention. By using such a remote study design, two studies assessing video quality ratings of similar conditions as in the previously mentioned dataset were conducted. These two studies allow to address three research topics that will be the focus of the remainder of this chapter. At first, it will be investigated whether video quality ratings obtained using the remote study design are comparable to those collected in the lab environment. Second, a comparison between video quality ratings collected using a stimulus duration of 20 s instead of 30 s will be performed, which tries to answer whether it is enough to use a shorter stimulus duration as proposed in ITU-T Rec. P.809. Lastly, the differences between using a discrete 5-point ACR scale and the extended continuous 7-point scales will be investigated. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
During the Covid-19 pandemic, most schools had to adopt online learning, which is a special kind of e-learning that provides a virtual classroom via a live session for both teachers and learners. However, studies on education in the Covid-19 pandemic shows that there should be more efforts from researchers as well as governments to effectively support learners. In this paper, we focus on the problem of Quality of Experience in online learning. We discuss the enabling technologies of online learning. Also, we make an extensive review of QoE in video streaming, the key enabling technology of online leaning. Finally, the key challenges and potential solutions of QoE management for future online learning will be discussed. © 2022 IEEE.
ABSTRACT
The rise of technological adoption of video-based information distribution accelerated by the COVID-19 pandemic leads to a substantial increase in video streaming on-demand service users. Video streaming services such as Twitch, and YouTube have enormous amount of active users so the system developers need to develop a system capable of handling numerous requests. The increase of video streaming on-demand service users poses a challenge for system developers to guarantee the Quality of Experience (QoE) for all system users. Said surge of users also resulted in high resource usage to serve each user while securing QoE. In order to ensure the QoE on video streaming on-demand service, this work develops the system using the waterfall model that implements a distributed approach for the video streaming service system. The system has reverse proxy and load balancing capabilities to forward and equally distribute HTTP requests and uses DASH to segment the video file to stream the video without sending the whole file. The developed system for this work subjectively ensures the QoE with a mean opinion score (MOS) of 4.434 from 53 respondents. © 2022 IEEE.
ABSTRACT
In recent years, virtual reality (VR) has gradually entered the daily education and teaching activities from pure scientific research. In the area of assistance teaching, some typical computer softwares still play some important roles. This makes remote teaching activities can just learn voice, yet cannot possess the feeling of realistic existence. Especially in scenario of COVID-19, remote teaching activities with proper perceptibility are in urgent demand. To deal with the current challenge, this paper proposes a wireless VR-based multimedia-assisted teaching system framework under mobile edge computing networks. In this framework, cooperative edge caching and adaptive streaming based on viewport prediction are adopted to jointly improve the quality of experience (QoE) of VR users. First, we investigated the resource management problem of caching and adaptive streaming in this framework. Considering the complexity of the formulated problem, a distributed learning scheme is proposed to solve the problem. The experimental data are verified and the experimental results prove that the studied methods improve the performance of user QoE. [ FROM AUTHOR]
ABSTRACT
Virtual reality (VR) applications, especially those where the user is untethered to a computer, are becoming more prevalent as new hardware is developed, computational power and artificial intelligence algorithms are available, and wireless communication networks are becoming more reliable, fast, and providing higher reliability. In fact, recent projections show that by 2022 the number of VR users will double, suggesting the sector was not negatively affected by the worldwide COVID-19 pandemic. The success of any immersive communication system is heavily dependent on the user experience it delivers, thus now more than ever has it become crucial to develop reliable models of immersive media experience (IMEx). In this paper, we survey the literature for existing methods and tools to assess human influential factors (HIFs) related to IMEx. In particular, subjective, behavioural, and psycho-physiological methods are covered. We describe tools available to monitor these HIFs, including the user's sense of presence and immersion, cybersickness, and mental/affective states, as well as their role in overall experience. Special focus is placed on psycho-physiological methods, as it was found that such in-depth evaluation was lacking from the existing literature. We conclude by touching on emerging applications involving multiple-sensorial immersive media and provide suggestions for future research directions to fill existing gaps. It is hoped that this survey will be useful for researchers interested in building new immersive (adaptive) applications that maximize user experience.
ABSTRACT
In this chapter, research about the assessment of video quality for gaming content will be provided. At first, a dataset that was used for the development of the ITU-T Rec. G.1072 will be presented. The dataset was created in a laboratory environment using the passive test paradigm described in Chap. 3. Next, some results of the collected video quality ratings will be illustrated. While QoE assessment studies traditionally make use of controlled laboratory environments, there are also other possibilities to conduct user studies without a laboratory environment. Especially during the COVID-19 pandemic, which prevented many researchers from performing lab studies, the concept of supervised and unsupervised remote studies got lots of attention. By using such a remote study design, two studies assessing video quality ratings of similar conditions as in the previously mentioned dataset were conducted. These two studies allow to address three research topics that will be the focus of the remainder of this chapter. At first, it will be investigated whether video quality ratings obtained using the remote study design are comparable to those collected in the lab environment. Second, a comparison between video quality ratings collected using a stimulus duration of 20 s instead of 30 s will be performed, which tries to answer whether it is enough to use a shorter stimulus duration as proposed in ITU-T Rec. P.809. Lastly, the differences between using a discrete 5-point ACR scale and the extended continuous 7-point scales will be investigated. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The turn of the decade introduced a new era of global pandemics to the world through the appearance of COVID-19, which is still an active crisis at the time of this paper. As a countermeasure, the phenomena of home office and online education became not only widely available, but also mandatory in many countries. However, the performance, reliability and general usability of such real-time activities may be severely affected by unfavorable network conditions. In both contexts, content sharing is now a common practice, and the success of the related use cases may fundamentally depend on it. In this paper, we present our surveys and subjective studies on the Quality of Experience of content sharing in online education and online meetings. A total of 6 surveys and 5 experiments are detailed, addressing topics of student experience, user interface settings, sharing options of lecturers and employees of the private sector, the perceivable effects of network impairments and the related long-term adaptation, the rubber band effect of slide sharing, the overall perceived quality and the separate quality aspects of media loading times, and the preference between visual quality, average frame rate and frame rale uniformity. The findings of the subjective studies do not characterize the use cases of the investigated topics on a general, widely-applicable level, as only a single online platform is involved throughout the experiments. However, their experimental configurations are reinforced by comprehensive surveys and many results indicate statistically significant differences between the selected lest conditions.
ABSTRACT
Fueled by advances in multi-party communications, increasingly mature immersive technologies being adopted, and the COVID-19 pandemic, a new wave of social virtual reality (VR) platforms have emerged to support socialization, interaction, and collaboration among multiple remote users who are integrated into shared virtual environments. Social VR aims to increase levels of (co-)presence and interaction quality by overcoming the limitations of 2D windowed representations in traditional multi-party video conferencing tools, although most existing solutions rely on 3D avatars to represent users. This article presents a social VR platform that supports real-time volumetric holographic representations of users that are based on point clouds captured by off-the-shelf RGB-D sensors, and it analyzes the platform's potential for conducting interactive holomeeting scenarios (i.e., holoconferencing sessions for remote gathering and formal discussion). This work evaluates such a platform's performance and readiness for conducting holomeetings with up to four users, and it provides insights into aspects of the user experience when using single-camera and low-cost capture systems in scenarios with both frontal and side viewpoints. Overall, the obtained results confirm the platform's maturity and the potential of holographic communications for conducting interactive multi-party meetings, even when using low-cost systems and single-camera capture systems in scenarios where users are sitting or have a limited translational movement along the X, Y, and Z axes within the 3D virtual environment (commonly known as 3 Degrees of Freedom plus, 3DoF+).
ABSTRACT
Recently, the usage of 360-degree videos has prevailed in various sectors such as education, real estate, medical, entertainment and more. The development of the Virtual World “Metaverse” demanded a Virtual Reality (VR) environment with high immersion and a smooth user experience. However, various challenges are faced to provide real-time streaming due to the nature of high-resolution 360-degree videos such as high bandwidth requirement, high computing power and low delay tolerance. To overcome these challenges, streaming methods such as Dynamic Adaptive Streaming over HTTP (DASH), Tiling, Viewport-Adaptive and Machine Learning (ML) are discussed. Moreover, the superiorities of the development of 5G and 6G networks, Mobile Edge Computing (MEC) and Caching and the Information-Centric Network (ICN) approaches to optimize the 360-degree video streaming are elaborated. All of these methods strike to improve the Quality of Experience (QoE) and Quality of Service (QoS) of VR services. Next, the challenges faced in QoE modeling and the existing objective and subjective QoE assessment methods of 360-degree video are presented. Lastly, potential future research that utilizes and further improves the existing methods substantially is discussed. With the efforts of various research studies and industries and the gradual development of the network in recent years, a deep fake virtual world, “Metaverse” with high immersion and conducive for daily life working, learning and socializing are around the corner.
ABSTRACT
Tele-education was already a solution for people who cannot attend lessons in person (such as inaccessibility in rural areas or illness issues). However, COVID has revealed problems in tele-education with current technology, causing adolescents and children to slow down their learning curves and experience problems of social distancing with their classmates. This paper presents a user study to validate an immersive communication system for tele-education purposes. This system streams in real time a class using 360-degree cameras, allowing remote students to explore the whole scene and improving the feeling of being in the classroom with their colleagues. Additionally, the prototype provides notifications to the remote students about events (such as a changes in the teacher's presentation or classmates raising their hands) that occur outside their viewport to indicate in which direction they should move their heads to visualize them. To validate the system and investigate its possible added value, socioemotional factors such as presence, perceived quality, usability, and usefulness of the notifications were evaluated through a user test using questionnaires. The obtained results show that using immersive tele-education systems can improve the presence, as well as the benefits of the notifications on the experience of the remote students. © 2022 ACM.
ABSTRACT
Official statistics indicate that internet users all around the world watch more videos and play more games during the COVID-19 pandemic than at any time [18]. This unprecedented, challenging situation demands solutions to accommodate rapid growth while maintaining and/or enhancing the video quality. This paper proposes SODA-Stream, an SDN-based optimization framework for enhancing Quality-of-Experience (QoE) in DASH streaming. The optimization framework max-imizes the number of concurrent streaming sessions that can be accommodated in a network and maximize streaming quality. The practical implementation of the framework utilizes the dynamic routing and bandwidth allocation enabled by Software Defined Networking (SDN). The evaluation results show that SODA-Stream significantly outperforms the conventional network routing and resource allocation algorithms, accepting 52% more sessions, 45% improvement in bandwidth allocation, and 70% reduction in bandwidth wastage, smoother playback, and better viewing experience. © 2022 IEEE.
ABSTRACT
Students' demand for online learning has exploded during the post-COVID-19 pandemic era. However, due to their poor learning experience, students' dropout rate and learning performance of online learning are not always satisfactory. The technical advantages of Beyond Fifth Generation (B5G) can guarantee a good multimedia Quality of Experience (QoE). As a special case of multimedia services, online learning takes into account both the usability of the service and the cognitive development of the users. Factors that affect the Quality of Online Learning Experience (OL-QoE) become more complicated. To get over this dilemma, we propose a systematic scheme by integrating big data, Machine Learning (ML) technologies, and educational psychology theory. Specifically, we first formulate a general definition of OL-QoE by data analysis and experimental verification. This formula considers both the subjective and objective factors (i.e., video watching ratio and test scores) that most affect OL-QoE. Then, we induce an extended layer to the classic Broad Learning System (BLS) to construct an Extended Broad Learning System (EBLS) for the students' OL-QoE prediction. Since the extended layer can increase the width of the BLS model and reduce the redundant nodes of BLS, the proposed EBLS can achieve a trade-off between the prediction accuracy and computation complexity. Finally, we provide a series of early intervention suggestions for different types of students according to their predicted OL-QoE values. Through timely interventions, their OL-QoE and learning performance can be improved. Experimental results verify the effectiveness of the proposed scheme.
ABSTRACT
Since the outbreak of the COVID-19 pandemic, videoconferencing has become the default mode of communication in our daily lives at homes, workplaces and schools, and it is likely to remain an important part of our lives in the post-pandemic world. Despite its significance, there has not been any systematic study characterizing the user-perceived performance of existing videoconferencing systems other than anecdotal reports. In this paper, we present a detailed measurement study that compares three major videoconferencing systems: Zoom, Webex and Google Meet. Our study is based on 62 hours' worth of more than 1.1K videoconferencing sessions, which were created with a mix of emulated videoconferencing clients deployed in the cloud, as well as real mobile devices running from a residential network over two separate periods with nine months apart. We find that the existing videoconferencing systems vary in terms of geographic scope and resource provisioning strategies, which in turns determine streaming lag experienced by users. We also observe that streaming rate can change under different conditions (e.g., available bandwidth, number of users in a session, mobile device status), which affects user-perceived streaming quality. Beyond these findings, our measurement methodology enables reproducible benchmark analysis for any types of comparative or longitudinal study on available videoconferencing systems.
ABSTRACT
This article presents the findings of an unsupervised field study conducted over 31 days in Argentina and Cuba in order to explore mobile QoE. We also analyzed the correspondence between the objective observations and users’ opinions to identify contextual conditions that could have influenced the study’s results. Using their own mobile devices in everyday contexts, 95 users from Argentina and Cuba interacted with CovidInfo app, thereby recording values for eight objective metrics as well as their opinions. The total data set collected consisted of 41,144 records. Analysis of the CovidInfo application yielded positive QoE results in both countries, as 88% and 70% of the objective metrics in Argentina and Cuba, respectively, were optimal. The main difference between the two countries is the type of connection: In Argentina, WiFi networks are predominant, while the trend in Cuba is mobile data connections. Alternate : Este artículo presenta los hallazgos de un estudio de campo no supervisado realizado durante 31 días en Argentina y Cuba con el fin de explorar la QoE móvil. También analizamos la correspondencia entre las observaciones objetivas y las opiniones de los usuarios para identificar las condiciones contextuales que podrían haber influido en los resultados del estudio. Utilizando sus propios dispositivos móviles en contextos cotidianos, 95 usuarios de Argentina y Cuba interactuaron con la app CovidInfo, registrando así los valores de ocho métricas objetivas, así como sus opiniones. El conjunto total de datos recopilados consistió en 41.144 registros. El análisis de la aplicación CovidInfo arrojó resultados positivos de QoE en ambos países, ya que el 88% y el 70% de las métricas objetivas en Argentina y Cuba, respectivamente, fueron óptimas. La principal diferencia entre los dos países es el tipo de conexión: en Argentina predominan las redes WiFi, mientras que la tendencia en Cuba son las conexiones de datos móviles.
ABSTRACT
In the methodology part of this study, we introduced a combined “AHP(blocks) and Entropy” approach for improving of Multi-Criteria Decision Making (MCDM) for Cloud Services (CS) selection. On this basis, we optimize the combination of professional experts’ opinion and objectivity of entropy criteria weighting. In this second part, we test our findings on an existing universal dataset with Quality of Service (QoS) and Quality of Experience (QoE) criteria and adapting for the needs of individual users and small education organisations under conditions of COVID-19 pandemics. © 2022, Springer Nature Switzerland AG.
ABSTRACT
Mobile applications are increasingly present in most aspects of our daily life, satisfying the needs of information, communication, and entertainment. Different factors can degrade the performance of these applications or services, and users can undergo unsatisfactory experiences. To achieve acceptance of mobile applications by the user, it is necessary to consider the Quality of Experience, which allows to measure the quality of an application based on the user’s perception. This work presents AMobileQoE, an SPL framework to automatically generate the code necessary to analyse the QoE of mobile applications following the Design Science Research approach. It enables developers to generate more than a thousand different product configurations to measure QoE from a set of network, device, and user metrics. The CovidInfo application was developed to test the SPL framework in a real context with real users, showing the ease of integration and the lightness of the framework. Furthermore, a quality assessment was performed on AMobileQoE showing a high degree of modularity and variability. © 2022 River Publishers.
ABSTRACT
This paper provides a subjective analysis of streaming video to understand human visual Quality of Experience (QoE). The emergence of the Corona pandemic has prompted a rise in demand for internet services, particularly for video streaming. A limited number of companies in Europe choose to reduce video transmission quality to escape Internet strain. The QoE markers are defined as either satisfactory if the video loads in less than 10 seconds and plays out efficiently or undesirable if the loading time is above 10 seconds or the playout suffers from stands. Online media will display various kinds of high motion sports and music videos. Such metrics would enable internet providers to assess the degree to which the QoE of video streaming customers can decline under bad network conditions. Video quality is influenced by consumer behavior and content and network and device efficiency. A more significant number of content types would help us to study human viewer behavior better. Network providers typically do not see this kind of traffic. QoE is now believed to be an essential aspect of the reactive traffic control mechanism within network operators. The researcher looked for the best to create a database that contains as many subject forms as possible. The mean opinion score (MOS) has an exponential relationship with the video playout's number of stalls. Today's Global Adaptive Streaming over HTTP (DASH) definition to distribute the content-based streaming services employ differential bitrate techniques. QoE tracking often considers a variety of video QoE influence factors (IFs) and contableuration schemes. Multimedia information QoE can be assessed using subjective or objective methods. © 2021 IEEE
ABSTRACT
Remote working and distance learning has become the new normal due to the COVID-19 epidemic that has affected the whole world. In a short time, the security, functionality and performance of existing commercial and open source video conferencing software have started to be compared and discussed. When video conferencing products are examined, it is seen that most of them are developed by WebRTC technology. WebRTC is an open source software library with a 10-year history that enables real-Time audio and video sharing. Its real-Time nature, access from different types of devices and heterogeneous networks, and the variety of data types make it difficult to test WebRTC products in a standard way or with a standard tool. In this study, functionality, performance and usability test of video conferencing products developed by webRTC are told in terms of domestic products. Due to the fact that the demand for these products will increase in the forthcoming years, test methods and emerged results told in this study will gain more importance. © 2021 IEEE.
ABSTRACT
Mobile broadband (MBB) is one of the critical goals in fifth-generation (5G) networks due to rising data demand. MBB provides very high-speed internet access with seamless connections. Existing MBB, including third-generation (3G) and fourth-generation (4G) networks, also requires monitoring to ensure good network performance. Thus, performing analysis of existing MBB assists mobile network operators (MNOs) in further improving their MBB networks’ capabilities to meet user satisfaction. In this paper, we analyzed and evaluated the multidimensional performance of existing MBB in Oman. Drive test measurements were carried out in four urban and suburban cities: Muscat, Ibra, Sur and Bahla. This study aimed to analyze and understand the MBB performance, but it did not benchmark the performance of MNOs. The data measurements were collected through drive tests from two MNOs supporting 3G and 4G technologies: Omantel and Ooredoo. Several performance metrics were measured during the drive tests, such as signal quality, throughput (downlink and unlink), ping and handover. The measurement results demonstrate that 4G technologies were the dominant networks in most of the tested cities during the drive test. The average downlink and uplink data rates were 18 Mbps and 13 Mbps, respectively, whereas the average ping and pong loss were 53 ms and 0.9, respectively, for all MNOs.