Relay station selection and power allocations for Multiple Description-Coded video in wireless mesh networks
Bibliographic record
Abstract
Video transport in wireless mesh networks is a challenging problem because of frequent link failures, limited link capacity, and multihop communications. However, the mesh topology provides some degree of freedom in designing error resilient video broadcasting scheme, as multiple links can exist between a source and a destination. In this paper, we combine the mesh nature with Multiple Description Coding (MDC) technique to design a video broadcasting scheme. An Access Point (AP) is broadcasting video traffic to the Mobile Stations (MSs) via a number of Relay Stations (RSs). The AP utilizes MDC technique to encode video traffic into equal descriptions. Each description is multicasted to several RSs, which further broadcast the descriptions to MSs. Whether or not an MS can successfully receive a description from an RS depends on the transmission power of the RS and the channel conditions between the RS and the MS. For each MS, the quality of the received video depends on the total number of correctly received descriptions. We study how to allocate the transmission power of the RSs so that to satisfy the quality of the received video at each MS while minimizing the maximum transmission power at the RSs. An optimization problem is first formulated, and then a heuristic power adjustment scheme is proposed to find the transmission power of each RS. Our numerical results show a good match between optimal solution and proposed heuristic.
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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 | 0.005 |
| 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 | 0.000 |
| 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".