QoE beyond the MOS: an in-depth look at QoE via better metrics and their relation to MOS

Hoßfeld, Tobias; Heegaard, Poul E.; Varela, Martín; Möller, Sebastian

doi:10.1007/s41233-016-0002-1

Cited by 101 publications

(69 citation statements)

References 30 publications

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“…However, other QoE metrics (like median, quantiles, etc.) may be especially of interest for service providers [10] which may be reflected by Q. Typically, the QoE of the entire session per user is modeled.…”

Section: Definition Of the Qoe Fairness Index A Notionmentioning

confidence: 99%

Definition of QoE Fairness in Shared Systems

HoBfeld¹,

Skorin‐Kapov²,

Heegaard³

et al. 2017

Preprint

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Abstract-User-centric service and application management focuses on the Quality of Experience (QoE) as perceived by the end user. Thereby, the goal is to maximize QoE while ensuring fairness among users, e.g., for resource allocation and scheduling in shared systems. Although the literature suggests to consider consequently QoE fairness, there is currently no accepted definition of QoE fairness. The contribution of this paper is the definition of a generic QoE fairness index F which has desirable key properties as well as the rationale behind it. By using examples and a measurement study involving multiple users downloading web content over a bottleneck link, we differentiate the proposed index from QoS fairness and the widely used Jain's fairness index. Based on results, we argue that neither QoS fairness nor Jain's fairness index meet all of the desirable QoE-relevant properties which are met by F . Consequently, the proposed index F may be used to compare QoE fairness across systems and applications, thus serving as a benchmark for QoE management mechanisms and system optimization.

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Section: Definition Of the Qoe Fairness Index A Notionmentioning

confidence: 99%

Definition of QoE Fairness in Shared Systems

HoBfeld¹,

Skorin‐Kapov²,

Heegaard³

et al. 2017

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…We must emphasize that the approach used here for simulating user diversities is supported by the literature [6]. It is based on real subjective data, and has been shown to lead to very good approximations.…”

Section: A Two Users Scenario: Optimal Qoe Resource Assignmentmentioning

confidence: 91%

“…While the majority of studies to-date addressing QoE estimation models rely on MOS values derived from subjective data, other studies have shown that different metrics beyond MOS may be of potential interest to service/network providers [6]. Statistical measures providing insight into score distributions and quantiles give providers a clearer view of how quality is actually perceived by the user population, rather than estimating the quality as perceived by an "average" user.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The set y x is an empirical user rating distribution for QoS test condition x over the k subjects. Since k is typically a small number, instead of using the empirical distribution, the user rating distribution can be modeled by use of a Truncated Normal distribution, U ∼ N (µ, σ, L, H), which has been shown to be valid in [6], with probability density function:…”

Section: B Modeling User Rating Distributionsmentioning

confidence: 99%

“…Previous studies have argued that objective QoE models based on Mean Opinion Scores (MOS) may be ill-suited for QoE management purposes, as variations between users are averaged out [4], [5]. Thus considering a service provider point of view, other QoE metrics, e.g., rating distributions or quantiles, may provide more meaningful input [6]. Given a QoE optimization problem (e.g., deciding on an optimal resource allocation, service configuration, adaptive video playout strategy, etc.)…”

Section: Introductionmentioning

confidence: 99%

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No silver bullet: QoE metrics, QoE fairness, and user diversity in the context of QoE management

Hosfeld¹,

Heegaard

Skorin‐Kapov

et al. 2017

2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX)

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Managing QoE is one of the most interesting direct applications of workable QoE models. Indeed, being able to predict how users perceive the quality of a service allows the service provider(s) to optimize its delivery, based on several possible criteria. It has been argued, however, that the MOS is illsuited for this type of application, and that different measurese.g., rating distributions or quantiles -are better suited for the task. In this paper we build on these ideas by adding the notions of QoE fairness (as opposed to QoS fairness) and user diversity, and discuss how the choice of measures used, the importance of fairness, and how the variations between users can affect the optimal QoE management choices for service providers.

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Quality of Experience (QoE)‐based joint admission control and power allocation with guaranteed data rate

Zabetian

Khalaj

2023

Trans Emerging Tel Tech

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Service providers use objective models to map system quality of service (QoS) conditions to an estimated mean opinion score (MOS) in order to assess users' quality of experience (QoE). In contrast with earlier studies, we propose a hybrid model for call services that models the MOS in terms of the received signal strength indicator (RSSI) using a machine learning approach. Unlike most existing studies, which focus on maximizing the sum‐MOS of all users, we aim to maximize the average number of satisfied users in order to allocate optimal power to each user while ensuring the minimum data rate for each of them. Simulation results show that the proposed hybrid model outperforms the conventional objective model in terms of MOS per user and the probability of user satisfaction. Furthermore, when compared to conventional sum‐MOS and sum‐rate maximization problems, users are more satisfied with the proposed problem. In addition, we will present a joint power allocation and admission control problem due to the limited power available to meet the needs of all users. The findings show a trade‐off between the number of admitted users and their level of satisfaction, giving operators valuable insight into how to better utilize their network resources.

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QoE beyond the MOS: an in-depth look at QoE via better metrics and their relation to MOS

Abstract: Abstract-This

Cited by 101 publications

References 30 publications

Definition of QoE Fairness in Shared Systems

Definition of QoE Fairness in Shared Systems

No silver bullet: QoE metrics, QoE fairness, and user diversity in the context of QoE management

Quality of Experience (QoE)‐based joint admission control and power allocation with guaranteed data rate

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