An adaptation mechanism for robust OTT video transmission
Bibliographic record
Abstract
“Over the top” (OTT) has become an increasingly popular form of delivery for voice and video communications. Ubiquitous OTT communications, pioneered by Skype, have, with the recent release of WebRTC, become a commodity readily available for various applications, without a need for extra installations. Still, in spite of such progress, media communications remain challenging as user expectations are keeping ahead of technology progress and portable devices are becoming the norm. In practical terms, this means that media flows must constantly adapt to varying network conditions to try to -reliably!- offer the best experience to the users. We present in this paper a sender-side adaptation algorithm for video flows which trades off redundancy and throughput to maximize quality while minimizing the likelihood of loss, thus preserving service continuity. By exploiting standard FEC mechanisms at different levels of redundancy while controlling the quality of media delivered, we create a discrete parameter space which the algorithm exploits based on quality feedback. We study the performance of this algorithm in a number of reference test cases and discuss how it improves on earlier and related proposals.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".