A Loss-Based Utility Function for Predicting IPTV Quality of Experience over an Overlay Network
Bibliographic record
Abstract
Internet Protocol Television (IPTV) over broadband networks offers more services and flexibility, such as time-shifted TV and video-on-demand (VoD), than the traditional broadcast TV. Unfortunately, IP offers best effort service and Quality of Service (QoS) is not guaranteed. In order to provide QoS guarantees over the Internet, overlay networks are used. Impairments happening at the network layer such as delays and packet losses significantly affect the quality of the IPTV stream. Hence, a reliable mechanism to monitor the Quality of Experience (QoE) of IPTV users is essential to an IPTV service provider in order to avoid customer churn. It is essential that an IPTV provider be able to monitor the QoE with minimal overhead. Therefore, we propose a utility based QoE monitoring approach based on statistical losses at the application layer. This paper also presents mathematical proofs and simulation results that confirm the suitability and effectiveness of our proposed method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".