A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction

Kougioumtzidis, Georgios; Poulkov, Vladimir; Zaharis, Zaharias D.; Lazaridis, Pavlos I.

doi:10.1109/access.2022.3149592

Cited by 44 publications

(25 citation statements)

References 140 publications

(168 reference statements)

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“…On the other hand, objective evaluation methods use mathematical models that act as quality metrics to measure the audio or video received quality, such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), etc. A list of subjective and objective QoE assessment metrics are listed in Tables 1 and 2, where more details and explanations about all these metrics can be found in [22,23]. Given that QoS primarily focuses on the technical elements, whereas QoE is centered around customer service and satisfaction, it is plausible to include QoS as an inherent component within the definition of QoE.…”

Section: Qoe Of Multimedia Communications In M-healthmentioning

confidence: 99%

“…Many studies have focused on determining the relationship between QoS parameters and QoE; few instances claim that QoS is the most critical factor in determining a user's perceived quality [56]. Correlation models must be established to evaluate the relationship between QoS parameters and their individual and mutual effects on QoE [22,57]. The correlation models proposed in the literature can be divided into general and specific models [58].…”

Section: The Correlation Between Qos and Qoe In M-health Applicationsmentioning

confidence: 99%

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Exploring the Role of 6G Technology in Enhancing Quality of Experience for m-Health Multimedia Applications: A Comprehensive Survey

Nasralla

Khattak

Rehman

et al. 2023

Sensors

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Mobile-health (m-health) is described as the application of medical sensors and mobile computing to the healthcare provision. While 5G networks can support a variety of m-health services, applications such as telesurgery, holographic communications, and augmented/virtual reality are already emphasizing their limitations. These limitations apply to both the Quality of Service (QoS) and the Quality of Experience (QoE). However, 6G mobile networks are predicted to proliferate over the next decade in order to solve these limitations, enabling high QoS and QoE. Currently, academia and industry are concentrating their efforts on the 6G network, which is expected to be the next major game-changer in the telecom industry and will significantly impact all other related verticals. The exponential growth of m-health multimedia traffic (e.g., audio, video, and images) creates additional challenges for service providers in delivering a suitable QoE to their customers. As QoS is insufficient to represent the expectations of m-health end-users, the QoE of the services is critical. In recent years, QoE has attracted considerable attention and has established itself as a critical component of network service and operation evaluation. This article aims to provide the first thorough survey on a promising research subject that exists at the intersection of two well-established domains, i.e., QoE and m-health, and is driven by the continuing efforts to define 6G. This survey, in particular, creates a link between these two seemingly distinct domains by identifying and discussing the role of 6G in m-health applications from a QoE viewpoint. We start by exploring the vital role of QoE in m-health multimedia transmission. Moreover, we examine how m-health and QoE have evolved over the cellular network’s generations and then shed light on several critical 6G technologies that are projected to enable future m-health services and improve QoE, including reconfigurable intelligent surfaces, extended radio communications, terahertz communications, enormous ultra-reliable and low-latency communications, and blockchain. In contrast to earlier survey papers on the subject, we present an in-depth assessment of the functions of 6G in a variety of anticipated m-health applications via QoE. Multiple 6G-enabled m-health multimedia applications are reviewed, and various use cases are illustrated to demonstrate how 6G-enabled m-health applications are transforming human life. Finally, we discuss some of the intriguing research challenges associated with burgeoning multimedia m-health applications.

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Section: Qoe Of Multimedia Communications In M-healthmentioning

confidence: 99%

Section: The Correlation Between Qos and Qoe In M-health Applicationsmentioning

confidence: 99%

Exploring the Role of 6G Technology in Enhancing Quality of Experience for m-Health Multimedia Applications: A Comprehensive Survey

Nasralla

Khattak

Rehman

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…It also discusses a few research questions, such as whether the cloud gaming (CG) QoE assessment model proposed in (Yang et al, 2019) can be used to measure OMG QoE, how important is jitter when exploring the impact of packet loss on gaming QoE, which features influenced the most to the overall gaming QoE during playing online mobile game, and how can gaming QoE be predicted using only network monitoring data. Some ML-based QoE predictive models for video gaming and multimedia applications are highlighted in (Huang et al, 2018;Anwar et al, 2020;Kougioumtzidis et al, 2022) but most of them are relying on application-level metrics. Moreover, they are not targeting 5G networks.…”

Section: Related Workmentioning

confidence: 99%

Modelling and Predicting Quality-of-Experience of Online Gaming Users in 5G Networks

Tan¹,

Lim²,

Diong³

2022

IJTech

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5G technology will greatly improve quality of human life by enabling new use cases that will fully leverage on the improved throughput, connections, and latency of the 5G networks. Enhanced Mobile Broadband (eMBB), which supports ultra-high throughput, is one of the most important features in 5G networks. This service is expected to improve users' quality of experience (QoE) when using resource-intensive and far more interactive applications such as playing online games. It is widely known that 5G networks can be used for gathering network monitoring data and application metrics; however, the correlation between the data and the users' QoE is not well understood. Since large amount of data can be collected, machine learning approach is well suited for predicting users' QoE when playing online games in 5G networks. In this paper, an artificial neural network (ANN) model is proposed to predict the users' QoE based on the network monitoring data of a 5G network during an online gaming session and the model's performance is evaluated. The ANN model consists of four layers which include one input layer, two hidden layers, and one output layer. The Unified Management Expert (UME) system is used to collect the network monitoring data from a 5G NSA indoor private campus network. The proposed ANN model achieves prediction accuracy of close to 80% using 30 most relevant features derived from the radio access network monitoring data.

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“…Therefore, it has become crucial to develop reliable models of immersive media QoE. While technological and contextual factors have been explored in the past (Wang et al, 2017;Metzger et al, 2018;Sousa et al, 2020;Kougioumtzidis et al, 2022), HIFs have been less explored, as highlighted by Moinnereau et al (2022a).…”

Section: Introductionmentioning

confidence: 99%

Instrumenting a virtual reality headset for at-home gamer experience monitoring and behavioural assessment

Moinnereau

Oliveira

Falk

2022

Front. Virtual Real.

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Measuring a gamer’s behaviour and perceived gaming experience in real-time can be crucial not only to assess game usability, but to also adjust the game play and content in real-time to maximize the experience per user. For this purpose, affective and physiological monitoring tools (e.g., wearables) have been used to monitor human influential factors (HIFs) related to quality of experience (QoE). Representative factors may include the gamer’s level of engagement, stress, as well as sense of presence and immersion, to name a few. However, one of the major challenges the community faces today is being able to accurately transfer the results obtained in controlled laboratory settings to uncontrolled everyday settings, such as the gamer’s home. In this paper, we describe an instrumented virtual reality (VR) headset, which directly embeds a number of dry ExG sensors (electroencephalography, EEG; electrocardiography, ECG; and electrooculography, EOG) to allow for gamer behaviour assessment in real-time. A protocol was developed to deliver kits (including the instrumented headset and controllers, laptop with the VR game Half-life Alyx, and a second laptop for data acquisition) to participants’ homes during the COVID-19 lockdown. A brief videoconference session was made to provide the participants with instructions, but otherwise the experiment proceeded with minimal experimenter intervention. Eight participants consented to participate and each played the game for roughly 1.5 h. After each gaming session, participants reported their overall experience with an online questionnaire covering aspects of emotions, engagement, immersion, sense of presence, motion sickness, flow, skill, technology adoption, judgement and usability. Here, we describe our obtained findings, as well as report correlations between the subjective ratings and several QoE-related HIFs measured directly from the instrumented headset. Promising results are reported.

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A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction

Cited by 44 publications

References 140 publications

Exploring the Role of 6G Technology in Enhancing Quality of Experience for m-Health Multimedia Applications: A Comprehensive Survey

Exploring the Role of 6G Technology in Enhancing Quality of Experience for m-Health Multimedia Applications: A Comprehensive Survey

Modelling and Predicting Quality-of-Experience of Online Gaming Users in 5G Networks

Instrumenting a virtual reality headset for at-home gamer experience monitoring and behavioural assessment

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