Data analysis on video streaming QoE over mobile networks

Wang, Qingyong; Dai, Hong-Ning; Wu, Di; Hong, Xia

doi:10.1186/s13638-018-1180-8

Cited by 15 publications

(4 citation statements)

References 18 publications

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“…The study also approaches multiple ML techniques for the prediction of MOS score and user engagement. These techniques include random forest, DT, SVM, kNN, and Naive Bayes (NB), the performance of In a recent study conducted in [44], a data analysis model is proposed to analyze the video based on MOS collected from various datasets. The dataset includes the MOS value of three QoE factors such as initial buffering, stalling time, and stalling ratio.…”

Section: B Ml-based Qoe Prediction Methodsmentioning

confidence: 99%

Subjective QoE of 360-Degree Virtual Reality Videos and Machine Learning Predictions

et al. 2020

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360-degree video provides an immersive experience to end-users through Virtual Reality (VR) Head-Mounted-Displays (HMDs). However, it is not trivial to understand the Quality of Experience (QoE) of 360-degree video since user experience is influenced by various factors that affect QoE when watching a 360-degree video in VR. This manuscript presents a machine learning-based QoE prediction of 360degree video in VR, considering the two key QoE aspects: perceptual quality and cybersickness. In addition, we proposed two new QoE-affecting factors: user's familiarity with VR and user's interest in 360-degree video for the QoE evaluation. To aim this, we first conduct a subjective experiment on 96 video samples and collect datasets from 29 users for perceptual quality and cybersickness. We design a new Logistic Regression (LR) based model for QoE prediction in terms of perceptual quality. The prediction accuracy of the proposed model is compared against well-known supervised machine-learning algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision Tree (DT) with respect to accuracy rate, recall, f1-score, precision, and mean absolute error (MAE). LR performs well with 86% accuracy, which is in close agreement with subjective opinion. The prediction accuracy of the proposed model is then compared with existing QoE models in terms of perceptual quality. Finally, we build a Neural Network-based model for the QoE prediction in terms of cybersickness. The proposed model performs well against the state of the art QoE prediction methods in terms of cybersickness.

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Section: B Ml-based Qoe Prediction Methodsmentioning

confidence: 99%

Subjective QoE of 360-Degree Virtual Reality Videos and Machine Learning Predictions

et al. 2020

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“…Such an issue has been addressed through the development of a new data framework based on video streaming QoE analysis that uses K-means clustering and logistic regression. According to intensive experiments on real datasets, the framework proposed in [17] surpassed the prediction accuracy of other methods. Another way to improve end users' video quality is to shift from traditional QoS video services to QoE-based video services delivery.…”

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

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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.

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“…The proposed algorithm has a precision of over 96%. The study covers batch clustering with a classification of the data collected from mobile networks [5]. The video ATLAS algorithm based on a support vector regressor (SVR) can assess the QoE level based on video quality, the occurrence of stallings as well as memory-related to the last distortion interval.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning Data-Driven Continuous QoE Assessment in Adaptive Streaming-Based Television System

Andruloniw

Kowalik²,

Zwierzykowski

2022

Applied Sciences

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The quality of experience (QoE) assessment of adaptive video streaming may be crucial for detecting degradations impacting customer satisfaction. In a telecommunication environment, eliminating failure points may be the highest priority. This study aims to assess the QoE level of the video played by the STB device connected to the production TV system. The evaluation has been based on the stalling effects, video quality changes, and the time related to the last decreased bitrate change occurrence. The two-phase continuous clustering approach has been studied to assess the QoE level based on the ACR scale. The number of devices with grades 1 or 2 is relatively low, but those devices generate significantly more events than adequately functioning devices. STBs try to play the highest possible bitrate, and there is no possibility of setting the intermediate bitrate level. The STB player does not have the button to set the quality level, usually available in pure over-the-top applications. Hence the bitrate fluctuations that can annoy customers appear for the lowest grades. The boundary cases can be easily assessed. The outcome should be challenged by the customers’ opinions to find the proper QoE threshold. Continuous clustering may allow telecom operators to assess customer satisfaction with their TV service.

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Data analysis on video streaming QoE over mobile networks

Cited by 15 publications

References 18 publications

Subjective QoE of 360-Degree Virtual Reality Videos and Machine Learning Predictions

Subjective QoE of 360-Degree Virtual Reality Videos and Machine Learning Predictions

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

Unsupervised Learning Data-Driven Continuous QoE Assessment in Adaptive Streaming-Based Television System

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