Heuristics for Jointly Optimizing FEC and ARQ for Video Streaming over IEEE802.11 WLAN
Bibliographic record
Abstract
In this paper, the problem of selecting an appropriate number of forward error correction (FEC) packets under given channel and network conditions is formulated as an optimization problem and two heuristic algorithms are proposed to find the solution. These heuristic algorithms are used for selecting the parameters of FEC scheme under a given maximum number of automatic repeat request (ARQ) retries, to achieve higher visual quality and efficient transport of multimedia applications over an IEEE 802.11 wireless channel. The proposed algorithms are: adaptive linear FEC (ALFEC) and adaptive exponential FEC (AEFEC), where the number of FEC packets respectively decreases linearly and exponentially, as the sender's queue increases. Simulated performance of the proposed algorithms is compared against those for the existing static and dynamic FEC generation schemes. It is found that the proposed heuristic algorithms outperform the other existing schemes in terms of system efficiency and provides comparable peak-signal-to-noise-ratio (PSNR) gain.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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".