Cross-Layer Routing for Multicasting Multiple Description Coded Media in Wireless Mesh Networks
Bibliographic record
Abstract
Delivering media traffic that requires very high bandwidth and good reconstruction quality in wireless mesh networks (WMNs) has been a challenging problem due to the limited bandwidth and multihop transmissions. In this paper, we study the problem of multicasting multiple description coded (MDC) media traffic in WMNs. A cross layer routing scheme is designed that builds multiple multicast trees, each of which delivers one description to the mobile stations (MSs) through multihop transmissions. 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 worst distortion at all MSs. A linear optimization problem is formulated for the routing and multicast-tree construction problem, and a heuristic scheme with much lower complexity is then proposed. Simulation results show that the proposed heuristic scheme achieves much lower min-max distortion than a two-step method for building multicast trees, and in some parameter settings can achieve close-to-optimum min-max distortion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".