VoIP-based calibration of the DQX model

Tsiaras, Christos; Rosch, Manuel; Stiller, Burkhard

doi:10.1109/ifipnetworking.2015.7145309

Cited by 14 publications

(6 citation statements)

References 3 publications

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“…The overall analysis of the DQX model enables the use of multiple and diverse parameters and explains how the parameters can affect the perceived QoE positively or negatively in a specific situation . A broader perspective of the DQX model was applied on the Voice-over Internet protocol-based (VOIP) using an experimental set up to capture all the end-users of QoE data in a VOIP services (C. Tsiaras, Rösch, & Stiller, 2015).…”

Section: = Equationmentioning

confidence: 99%

“…Evidently, the idea of the DQX model supports that QoE perceived can be optimized to determine the actual perceived QoE, because it supports the use of multiple QoS parameters along with other QoE influence factors with regards to the minimum, maximum, expected variable values and variable weights to enable the modelling of the perceived QoE (Aroussi & Mellouk, 2016;. Despite the importance of the DQX model to determine the expected MOS which represents the perceived QoE, it has not been applied in the mobile environment that comprises of a large scale scenerio (C. Tsiaras et al, 2015;.…”

Section: = Equationmentioning

confidence: 99%

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Framework for Modelling Mobile Network Quality of Experience Through Big Data Analytics Approach

Yusuf-Asaju

Dahalin

Ta’a

2017

JICT

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The increase in the usage of different mobile internet applications can cause deterioration in the mobile network performance. Such deterioration often declines the performance of the mobile network services that can influence the mobile Internet user's experience, which can make the internet users switch between different mobile network operators to get good user experience. In this case, the success of mobile network operators primarily depends on the ability to ensure good quality of experience (QoE), which is a measure of users' perceived quality of mobile Internet service. Traditionally, QoE is usually examined in laboratory experiments to enable a fixed contextual factor among the participants even though the results derived from these laboratory experiments presented an estimated mean opinion score representing perceived QoE. The use of user experience dataset involving time and location gathered from the mobile network traffic for modelling perceived QoE is still limited in the literature. The mobile Internet user experience dataset involving the time and location constituted in the mobile network can be used by the mobile network operators to make data-driven decisions to deal with disruptions observed in the network performance and provide an optimal solution based on the insights derived from the user experience data. Therefore, this paper proposed a framework for modelling mobile network QoE using the big data analytics approach. The proposed framework describes the process of estimating or predicting perceived QoE based on the datasets obtained or gathered from the mobile network to enable the mobile network operators effectively to manage the network performance and provide the users a satisfactory mobile Internet QoE.

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Section: = Equationmentioning

confidence: 99%

Section: = Equationmentioning

confidence: 99%

Framework for Modelling Mobile Network Quality of Experience Through Big Data Analytics Approach

Yusuf-Asaju

Dahalin

Ta’a

2017

JICT

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“…In support of the accurately and timely measurement of QoE for VoIP services, the state-of-the-art Web Real-Time Communications (WebRTC) technology was used to newly design and develop a VoIP client that collects directly all necessary user's feedback concerning the perceived communication quality, in experimental VoIP calls, under different network conditions in various scenarios [122] [137]. Those varying network conditions are emulated by the network emulation framework WANem [130], which utilizes a real network.…”

Section: Aqx In Voipmentioning

confidence: 99%

“…This thesis contributes in the following areas: QoE modeling [115] [140]; QoE model calibration [137] [138]; mobile call-termination monopoly termination [139]; and automatic/on-demand MvNO selection [135] [136]. These contributions do represent important and innovative advancements in both research and Mobile Termination Rates monopoly liberation.…”

mentioning

confidence: 99%

QoE awareness for the Mobile Termination Rates monopoly liberation

Tsiaras

Stiller

2017

2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)

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In mobile communication the mobile termination service involves charges between Mobile (Virtual) Network Operators (MvNOs). Furthermore, only the MvNO of the callee is able to terminate his calls. Thus, in the MvNOs call-termination market there is only one player profiting from Mobile Termination Rates (MTR); in turn this market is considered by regulators to be a "de-facto" monopoly, since the early days of the introduction of commercial mobile communication services. Given this monopoly fact, the only solution against a potential speculation by MvNOs was the regulation of the MTR. In a monopoly there is a corporation that is the only seller of a good or a service, and thus it can define the price. However, monopolies can be divided into two categories, the naturally defined and the market-defined monopolies. The power market in many countries is considered to be a natural monopoly, and the main reason is that there is usually only one power-wire reaching each house. Thus, only the company that owns the delivery network can provide power services. The termination service in mobile communication until the 4G is also considered to be a monopoly. However, this is a market-defined monopoly, since there is no physical limitation (e.g., wires) for reaching a mobile user.

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“…The rationale behind this approach is that the model should effectively reflect the situation when one variable's MOS is very low and cannot be compensated by an improvement of other variables. The authors further propose a deterministic QoE model where QoE may be calculated based on multiple QoS factors, with VoIP quality studies confirming exponential relationships and the potential of combining single variable equations in a multiplicative way [23].…”

Section: Integrating Multiplicative Termsmentioning

confidence: 99%

On Additive and Multiplicative QoS-QoE Models for Multiple QoS Parameters

Hoßfelt¹,

Skorin‐Kapov²,

Heegaard³

et al. 2016

5th ISCA/DEGA Workshop on Perceptual Quality of Systems (PQS 2016)

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Generic relationships between QoE and QoS have been intensively discussed in literature for single QoS parameters and often found to be logarithmic or exponential. While there are many experimental studies investigating statistically the influence of several parameters on QoE, the generic relationship between them, and how to best model it, have not been discussed so far. For communication networks, however, there is a major interest from different stakeholders to have multi-dimensional QoE models. The contribution of this paper is an analysis of the generic relationship between QoS and QoE for multiple QoS parameters and its implications. We address the question of whether multi-dimensional QoE models for several parameters are additive or multiplicative. In an analytic model and with examples involving HTTP non-adaptive video streaming, we show that a multiplicative model has different properties than the current additive QoE model proposed in ITU-T standards. We want to raise sensitivity in the community on multi-factor QoE models, their properties, and the need for multi-factor studies to confirm the appropriate models.

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VoIP-based calibration of the DQX model

Cited by 14 publications

References 3 publications

Framework for Modelling Mobile Network Quality of Experience Through Big Data Analytics Approach

Framework for Modelling Mobile Network Quality of Experience Through Big Data Analytics Approach

QoE awareness for the Mobile Termination Rates monopoly liberation

On Additive and Multiplicative QoS-QoE Models for Multiple QoS Parameters

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