Bibliographic record
Abstract
Existing video multicast routing protocols in wireless ad hoc networks have been developed under the assumption that destination nodes wish to receive all the information sent by the multicast source, i.e., they do not support heterogeneous destinations. This paper addresses the problem of video multicast for heterogeneous destinations in wireless ad hoc networks. Multiple Description Coding (MDC) is used for video coding. MDC generates multiple independent bit-streams, where the multiple bit-streams are referred to as multiple descriptions (MD). Furthermore, MDC enables a useful reproduction of the video when any description is correctly received. Specifically, we propose three novel multiple multicast trees routing protocols. The first protocol constructs multiple disjoint multicast trees and assigns MD video in a centralized fashion, and is referred to as Centralized MDMTR (Multiple Disjoint Multicast Trees Routing). The second protocol is a variant of Centralized MDMTR. We refer to it as Sequential MDMTR. The main difference between Sequential MDMTR and Centralized MDMTR is that, Sequential MDMTR sequentially assigns MD video to the destination nodes. In order to reduce construction delay and routing overhead, we further propose Distributed MDMTR protocol. Both protocols, Centralized MDMTR and Distributed MDMTR, exploit the independent-description property of MDC along with multiple disjoint paths to increase the number of assigned video descriptions to each destination. We extensively evaluate our proposed protocols by simulations and show that they outperform the existing work.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".