Subset Selection in Type-II Hybrid ARQ/FEC for Video Multicast
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes an error control scheme that minimizes the total distortion experienced by the receivers using a new version of Type-II hybrid ARQ/FEC. Based on the feedback information about the losses in the previous Group of Pictures (GOP), the server sends parity packets for a subset of the frames from that GOP with the aim of minimizing the total distortion experienced by the receivers. The subset selection problem is NP-hard, so we propose a suboptimal method to solve it based on simulated annealing. Experimental results for the case when a single parity packet is used per group of 16 packets show that the proposed subset selection improves the plain Type-II hybrid ARQ/FEC by over 4 dB in decoded video PSNR, and achieves a 1-1.5 dB gain compared to a state-of-the-art error control method based on rate-distortion optimized frame retransmission.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it