Bibliographic record
Abstract
Video dissemination over Vehicular Ad Hoc Networks is an attractive technology that supports many novel applications. The merit of this work lies in the design of an efficient video dissemination protocol that provides high video quality at different data rates for urban scenarios. Our objective is to improve received video quality while meeting delay and packet loss. In this work, we first employ a reliable scheme known as connected dominating set, which is an efficient receiver-based routing scheme for broadcasting video content. To avoid repeated computing of the connected dominating set, we add three statuses to each node. In nonscalable video coding, the distribution of lost frames can cause a major impact on video quality at the receiver's end. Therefore, for the second step, we employ Interleaving to spread out the burst losses and to reduce the influence of loss distributions. Although Interleaving can reduce the influence of cluster frame loss, single packet loss is also a concern due to collisions, and to intermittent disconnection in the topology. In order to fix these single packet losses, we propose a store-carry-forward scheme for the nodes in order to retransmit the local buffer stored packets. The results, when compared to the selected base protocols, show that our proposed protocol is an efficient solution for video dissemination over urban Vehicular Ad Hoc Networks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.390 | 0.199 |
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".