The Effect of Seeking Operation on QoE of HTTP Adaptive Streaming Services

Nunome, Toshiro; Tani, Hiroyuki

doi:10.5121/ijcnc.2017.9201

Cited by 2 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… The following feature selection techniques are present: Filter methods (40%), Wrapper methods (30%), and Embedded methods (30%). Feature extraction techniques are used for the creation of a new reduced dimension, which is the combination of all input IFs 27 , 28 , 38 – 48 , the selection of the most important IFs 6 , 44 , 49 , elimination of the interdependence of input vector 27 , 28 , 50 , QoE prediction based on classification 51 – 53 . There are some special application aims such as determining the weighted coefficients to be used within the mathematical model 50 .…”

Section: Releated Workmentioning

confidence: 99%

Dimensionality reduction of independent influence factors in the objective evaluation of quality of experience

Skaka-Čekić

Husić

Odžak

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Big Data analytics and Artificial Intelligence (AI) technologies have become the focus of recent research due to the large amount of data. Dimensionality reduction techniques are recognized as an important step in these analyses. The multidimensional nature of Quality of Experience (QoE) is based on a set of Influence Factors (IFs) whose dimensionality is preferable to be higher due to better QoE prediction. As a consequence, dimensionality issues occur in QoE prediction models. This paper gives an overview of the used dimensionality reduction technique in QoE modeling and proposes modification and use of Active Subspaces Method (ASM) for dimensionality reduction. Proposed modified ASM (mASM) uses variance/standard deviation as a measure of function variability. A straightforward benefit of proposed modification is the possibility of its application in cases when discrete or categorical IFs are included. Application of modified ASM is not restricted to QoE modeling only. Obtained results show that QoE function is mostly flat for small variations of input IFs which is an additional motive to propose a modification of the standard version of ASM. This study proposes several metrics that can be used to compare different dimensionality reduction approaches. We prove that the percentage of function variability described by an appropriate linear combination(s) of input IFs is always greater or equal to the percentage that corresponds to the selection of input IF(s) when the reduction degree is the same. Thus, the proposed method and metrics are useful when optimizing the number of IFs for QoE prediction and a better understanding of IFs space in terms of QoE.

show abstract

Section: Releated Workmentioning

confidence: 99%

Dimensionality reduction of independent influence factors in the objective evaluation of quality of experience

Skaka-Čekić

Husić

Odžak

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The effect of network´s QoS on user´s QoE for a mobile video streaming service using H.265/VP9 codec was investigated in [31], and an algorithm based on QoE aware association for 5G Heterogeneous Networks was proposed in [33]. Toshiro Nunome and Hiroaki Tani [42] developed an assessment multidimensional QoE of HTTP-based streaming in seeking operation to assess the influence of two transmission methods, namely progressive download, and adaptive bitrate streaming. A subjective experiment under several network load conditions and two contents revealed the adaptive bitrate streaming was not necessarily effective for QoE enhancement, since the effectiveness of the method depended mainly on the use of both system and network conditions.…”

Section: Related Workmentioning

confidence: 99%

An automated model for the assessment of QoE of adaptive video streaming over wireless networks

Taha

Ali

Lloret

et al. 2021

Multimed Tools Appl

View full text Add to dashboard Cite

Nowadays, heterogeneous devices are widely utilizing Hypertext Transfer Protocol (HTTP) to transfer the data. Furthermore, HTTP adaptive video streaming (HAS) technology transmits the video data over wired and wireless networks. In adaptive technology services, a client's application receives a streaming video through the adaptation of its quality to the network condition. However, such a technology has increased the demand for Quality of Experience (QoE) in terms of prediction and assessment. It can also cause a challenging behavior regarding subjective and objective QoE evaluations of HTTP adaptive video over time since each Quality of Service (QoS) parameter affects the QoE of end-users separately. This paper introduces a methodology design for the evaluation of subjective QoE in adaptive video streaming over wireless networks. Besides, some parameters are considered such as video characteristics, segment length, initial delay, switch strategy, stalls, as well as QoS parameters. The experiment's evaluation demonstrated that objective metrics can be mapped to the most significant subjective parameters for user's experience. The automated model could function to demonstrate the importance of correlation for network behaviors' parameters. Consequently, it directly influences the satisfaction of the end-user's perceptual quality. In comparison with other recent related works, the model provided a positive Pearson Correlation value. Simulated results give a better performance between objective Structural Similarity (SSIM) and subjective Mean Opinion Score (MOS) evaluation metrics for all video test samples.

show abstract

The Effect of Seeking Operation on QoE of HTTP Adaptive Streaming Services

Cited by 2 publications

References 15 publications

Dimensionality reduction of independent influence factors in the objective evaluation of quality of experience

Dimensionality reduction of independent influence factors in the objective evaluation of quality of experience

An automated model for the assessment of QoE of adaptive video streaming over wireless networks

Contact Info

Product

Resources

About