QOE model in cellular networks based on QOS measurements using Neural Network approach

Anchuen, Patikorn; Uthansakul, Peerapong; Uthansakul, Monthippa

doi:10.1109/ecticon.2016.7561318

Cited by 19 publications

(7 citation statements)

References 5 publications

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“…The BP‐PSO–based QoE assessment method proposed for video service over LET networks uses PSO‐based postprocessing to reduce the error between the predicted and subjective QoE values . The QoE/QoS correlation model of cellular networks is established by using two‐layer BP neural network . The SVR is used to evaluate QoE for VoIP applications in wireless networks in Charonyktakis et al In addition, the video quality metric and perceptual evaluation of speech quality are selected as objective assessment methods to measure video and voice quality, respectively.…”

Section: Numerical Results and Analysismentioning

confidence: 99%

“…Malekmohamadi et al adopted multilayer BP neural network to model the nonlinear relationship between QoE and QoS parameters for video streaming. The causal relationship between QoS and QoE was established by using multilayer perception neural network in Anchuen et al and Pierucci. Zheng et al proposed a QoE assessment method based on BP neural network for video service in the 3G LET networks, where the particle swarm optimization (PSO) is applied as the postprocessing of the neural network weights to reduce the errors between the predicted and subjective MOSs.…”

Section: Related Workmentioning

confidence: 99%

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Modular and deep QoE/QoS mapping for multimedia services over satellite networks

Wang

Huang

2018

Int J Communication

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Summary Satellite networks are promising alternatives to deal with the increasing traffic volume and provide universal access for end users. Quality of experience (QoE) as an aggregate of user perception and network quality will be a critical success factor for multimedia service delivery over satellite networks. Quality of experience prediction based on quality of service (QoS) has received much attention recently, but it has been little investigated in satellite networks. In this paper, on the basis of the modular neural network and deep belief networks (DBNs), we design a QoE/QoS mapping method for multimedia services over satellite networks to translate QoS parameters into user QoE. The task of QoE/QoS mapping for multimedia services is decomposed into several different subtasks by using traffic classification. Heterogeneous DBNs are used to learn the mapping relationships between QoE and QoS for different types of services (ie, subtask) simultaneously. An integrative approach based on relative distance is exploited to select subneural network(s) to predict resulting QoE collaboratively. To determine the weight parameters of the QoE/QoS correlation model, the dataset composed of QoS parameters and subjective opinion scores is built on the basis of satellite network simulator and subjective test. Finally, the effectiveness of our QoE/QoS mapping method is validated, and the relationship between adoption rating and opinion scores is analyzed.

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Section: Numerical Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Modular and deep QoE/QoS mapping for multimedia services over satellite networks

Wang

Huang

2018

Int J Communication

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“…Here, we consider the linearization of the system at the operating point of b f , at which the change rate of the buffer level v k is 0. At this operating point, the following relationship can be derived from (9).…”

Section: Conventional Bitrate Selection Methodsmentioning

confidence: 99%

“…Although the QoE can be quantified based on subjective evaluation methods, it is difficult to feed the evaluation results back to the system in real time. Therefore, some researchers proposed objective evaluation methods to estimate the QoE [9], [10].…”

Section: Introductionmentioning

confidence: 99%

QoE-Aware Stable Adaptive Video Streaming Using Proportional-Derivative Controller for MPEG-DASH

Sakamoto

Shobudani

Hotchi

et al. 2021

IEICE Trans. Commun.

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In video distribution services such as video streaming, the providers must satisfy the various quality demands of the users. One of the human-centric indexes used to assess video quality is the quality of experience (QoE). In video streaming, the video bitrate, video freezing time, and video bitrate switching are significant determiners of QoE. To provide high-quality video streaming services, adaptive streaming using the Moving Picture Experts Group dynamic adaptive streaming over Hypertext Transfer Protocol (MPEG-DASH) is widely utilized. One of the conventional bitrate selection methods for MPEG-DASH selects the bitrate such that the amount of buffered data in the playback buffer, i.e., the playback buffer level, can be maintained at a constant value. This method can avoid buffer overflow and video freezing based on feedback control; however, this method induces high-frequency video bitrate switching, which can degrade QoE. To overcome this issue, this paper proposes a bitrate selection method in an adaptive video steaming for MPEG-DASH to improve the QoE by minimizing the bitrate fluctuation. To this end, the proposed method does not change the bitrate if the playback buffer level is not around its upper or lower limit, corresponding to the full or empty state of the playback buffer, respectively. In particular, to avoid buffer overflow and video freezing, the proposed method selects the bitrate based on proportional-derivative (PD) control to maintain the playback buffer level at a target level, which corresponds to an upper or lower threshold of the playback buffer level. Simulations confirm that, the proposed method offers better QoE than the conventional method for users with various preferences.

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“…MNOs need to increase business returns in terms of Return on Investment (RoI) [1], [2]. To retain existing users and attract new users [3], MNOs must reach user satisfaction to improve the network quality based on the user-centric approach [4]. However, the Quality of Service (QoS) metric plays a vital role in measuring…”

Section: Introductionmentioning

confidence: 99%

QoE Analysis for Improving Multimedia Services Based on Different Operating Situations on Cellular Networks

Anchuen

Uthansakul

2021

IEEE Access

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Cellular networks are constantly evolving to support the new context and demands of users with increasing internet traffic. The key to a service provider's success is a direct response to endpoints to retain existing users and attract new users. For this reason, Mobile Network Operators (MNOs) must reach user satisfaction under a user-centered perspective with Quality of Experience (QoE) to achieve a Return on Investment (RoI). In the latest QoE modelling, our previous method performed QoE estimation and synthesis using an Artificial Neural Network (ANN). Nevertheless, such work did not prove the characteristics of the QoE model built on different working situations. The essential features of the area and user diversities need to be considered in detail to summarize the proper model. In this paper, the proposed framework focuses on analyzing QoE model characteristics under different operating situations for comprehensive implementation. The experimental results reveal that the created QoE models from areas of different densities affect the different predictive variables. Inter-Rater Reliability (IRR) analysis exposes the reliability of datasets gathered from users of different groups to distinguish the effects on the response of significant parameters. Additionally, the summary specifies the characteristics of the appropriate model by area and user group as a guideline for improving the multimedia services.

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QOE model in cellular networks based on QOS measurements using Neural Network approach

Cited by 19 publications

References 5 publications

Modular and deep QoE/QoS mapping for multimedia services over satellite networks

Modular and deep QoE/QoS mapping for multimedia services over satellite networks

QoE-Aware Stable Adaptive Video Streaming Using Proportional-Derivative Controller for MPEG-DASH

QoE Analysis for Improving Multimedia Services Based on Different Operating Situations on Cellular Networks

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