A Proposed QoE Prediction in Video Streaming Using ITU-T Standards and Machine Learning Approaches
Bibliographic record
Abstract
The Quality of Experience (QoE) has become a crucial research topic for network operators and video providers, since it directly measures customer satisfaction.Objective QoE Assessment outperforms subjective in cost and applicability.However, predicting the QoE remains a challenge due to the variety of its influencing factors.The P.1203 by the ITU-T has emerged as the first QoE standard in video streaming environments.This work aims to predict the next possible QoE degradation to avoid poor perceived quality.A low user interaction is simulated using Selenium to extract the ITU-T P.1203 video parameters.The obtained data, in addition to network data from a network prob, have been used to train Feed Forward Neural Network (FFNN) classifiers and several regression models.The QoE has been predicted using both classification and regression in three implementations, in addition to implementing feature selection for better feature space size and the prediction performance.The FFNN results have been evaluated using cross-validation accuracy, RMSE, and confusion matrices.Combining the ITU-T P.1203 standards with the advances of machine learning (ML) in this study provided high prediction accuracy levels that exceeded 88% and highlighted nine selected influencing factors that are highly impacting the delivery of video services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".