Distortion-Aware Multicasting of Multiple Description Coded Media in Wireless Mesh Networks
Bibliographic record
Abstract
Traditional routing methods for multicasting traffic are mainly for single sessions, and their performance for multicasting bandwidth intensive media traffic in wireless mesh networks (WMNs) is limited by the end-to-end throughput of multi-hop transmissions. In this paper, we extend our earlier work [1] for multicasting media traffic that is encoded using multiple description coding (MDC) in WMNs by introducing a fully distributed scheme. The scheme relies on communications between one-hop neighbors to multiple multicast trees, each of which is rooted from one AP and delivers one description to some of the mobile stations (MSs) via the relay stations (RSs). The distortion of recovered media at an MS is determined by which description or descriptions have been correctly received. Our objective is to minimize the maximum distortion of recovered media at the MSs. This is achieved by taking advantage of the reduced bandwidth requirement of each description and the diversity gained from delivering multiple descriptions through different paths. Numerical results show that the proposed distributed scheme achieves good performance compared to the optimum solution and outperforms multicasting the source traffic directly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".