A smartphone agent for qoe evaluation and user classification over mobile networks

Verdolini, Alessandro; Petrangeli, Stefano

doi:10.1109/qomex.2013.6603242

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…According to 15 and 16 , the output from any phase is to design a straightforward strategy for content adaptation and selection output for mobile devices. The subjective evaluation for QoE is accomplished by presenting specific stimulations to the human subject to acquire results or user feedback on VCS quality assessment 17 . User selection will be based on several characteristics, where the user must have any experience or knowledge using streaming on a smartphone device.…”

Section: User Qoe For Video Content Selection (Vcs) Framework Proposementioning

confidence: 99%

User Quality of Experience (QoE) Satisfaction for Video Content Selection (VCS) Framework in Smartphone Devices

et al. 2021

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Video streaming is widely available nowadays. Moreover, since the pandemic hit all across the globe, many people stayed home and used streaming services for news, education, and entertainment. However, when streaming in session, user Quality of Experience (QoE) is unsatisfied with the video content selection while streaming on smartphone devices. Users are often irritated by unpredictable video quality format displays on their smartphone devices. In this paper, we proposed a framework video selection scheme that targets to increase QoE user satisfaction. We used a video content selection algorithm to map the video selection that satisfies the user the most regarding streaming quality. Video Content Selection (VCS) are classified into video attributes groups. The level of VCS streaming will gradually decrease to consider the least video selection that users will not accept depending on video quality. To evaluate the satisfaction level, we used the Mean Opinion Score (MOS) to measure the adaptability of user acceptance towards video streaming quality. The final results show that the proposed algorithm shows that the user satisfies the video selection, by altering the video attributes.

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Section: User Qoe For Video Content Selection (Vcs) Framework Proposementioning

confidence: 99%

User Quality of Experience (QoE) Satisfaction for Video Content Selection (VCS) Framework in Smartphone Devices

et al. 2021

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“…Traditional QoE-based evaluation approaches could be divided into two kinds-subjective evaluation methods [16] and objective evaluation methods-while most of recent researches are based on a combining method, which utilizes some QoS parameters and the relations between QoS and QoE to achieve QoE evaluation on video networks. In [17], the authors utilized a network monitoring app to capture the objective network parameters of mobile terminals and to collect users' subjective perception data, which were then used to mine the relations between the downstream throughput as well as video loading time and QoE, respectively. [18] proposed a binary subjective measure with 'acceptable' and 'unacceptable' to investigate the relations between its binary measurements and QoE under different circumstances.…”

Section: Related Workmentioning

confidence: 99%

Quality of experience estimation with layered mapping for hypertext transfer protocol video streaming over wireless networks

Wang

Sun

Wang

et al. 2015

Int J Communication

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Hypertext transfer protocol (HTTP) video streaming has occupied more than half of mobile Internet traffic today. Quality of experience (QoE) estimation for HTTP video streaming is significantly meaningful for network operators, as they can utilize valuable network resources more efficiently based on the QoE estimated. But, it is very hard to estimate QoE accurately with theoretical methods. In this paper, we adopt three buffering-related application metrics, which have been verified to be the main factors that influence the QoE of HTTP videos, together with three fundamental quality of service (QoS) metrics in network layer, to estimate QoE. We suggest a two-layered mapping model to capture the correlation between network QoS and application QoS and that between application QoS and QoE. Then, we exploit four regression methods, that is, linear regression, back propagation neural network, general regression neural network and support vector regression (SVR), to investigate the mapping relations of the network metrics, the application buffering-related metrics and QoE. Experimental results demonstrate that general regression neural network and SVR perform almost equally well in fitting ability, and SVR performs best among the four methods in terms of generalization ability. Finally, we analyze the influences of different network QoS parameters on QoE based on the experimental results.KEY WORDS: quality of experience (QoE); HTTP video streaming; mobile Internet; support vector regression (SVR); general regression neural network (GRNN); back propagation neural network (BPNN); quality of service (QoS); linear regression (LR)

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“…In Ref. [4], a kind of software, which can detect the network parameters of mobile terminals and push brief questions, is introduced. The author utilizes this software to collect experimental data and proposes functions to describe the relations between the downstream throughput as well as video loading time and QoE respectively.…”

mentioning

confidence: 99%

A new three-layer QoE modeling method for HTTP video streaming over wireless networks

Wang

Zhang

2014

2014 4th IEEE International Conference on Network Infrastructure and Digital Content

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Quality of experience (QoE) indicates users' perception of an Internet service or business in objective conditions, which is significantly meaningful to service providers or network operators to improve their quality of services and to utilize valuable network resources efficiently. Online video service has become the most fast-growing Internet service in recent years. Building models for QoE evaluation of video services has become a hot research topic. In this paper, we propose three metrics to evaluate buffering situations of HTTP video streaming, which have been verified to be the main factors that influence the QoE of network videos, and together with three fundamental quality of service (QoS) parameters, to build a three-layer QoE model for HTTP video streaming. Then, we use linear regression and BPNN (back propagation neural network) to reflect the mapping relations of the QoS parameters, buffering-related metrics and QoE, and demonstrate that BPNN performs better than linear regression in this situation.

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A smartphone agent for qoe evaluation and user classification over mobile networks

Cited by 4 publications

References 5 publications

User Quality of Experience (QoE) Satisfaction for Video Content Selection (VCS) Framework in Smartphone Devices

User Quality of Experience (QoE) Satisfaction for Video Content Selection (VCS) Framework in Smartphone Devices

Quality of experience estimation with layered mapping for hypertext transfer protocol video streaming over wireless networks

A new three-layer QoE modeling method for HTTP video streaming over wireless networks

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