Unequal channel error protection of multiple description codes for wireless media streaming
Bibliographic record
Abstract
We investigate the problem of optimal channel protection of multiple-description coded (MDC) multimedia contents at a wireless access point (WAP). For each MDC packet, the WAP has the option to protect and broadcast the packet using one of the available channel coders, or to drop the packet altogether. For a fixed FEC-based MDC, we show how this optimization can be approximated by a convex optimization problem with linear constraints, and thus, can be solved efficiently. We verify the validity of our results through ex- tensive simulations of a wireless image streaming application that employs multiple turbo channel coders, where we report gains of more than 2.50 dB PSNR in average reconstruction quality at receivers. We also devise an iterative algorithm for joint optimization of the channel code rate assignment and the design of the MDC. Our simulations show further gains of up to 5.40 dB in average PSNR when this joint optimization is employed.
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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 | 0.000 | 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 | 0.001 |
| 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".