Video Streaming Over Vehicular Ad Hoc Networks Using Erasure Coding
Bibliographic record
Abstract
Video streaming over vehicular ad hoc networks (VANETs) is an attractive application that provides many useful services for drivers. However, many problems need resolving, most of which are related to packet delay and packet loss. Due to the specific environment of VANETs, it is difficult to apply conventional protocols originally developed for the Internet, such as Real-time Transport Protocol (RTP). Previous research works have offered many new protocols to solve this problem. However, most of them cannot make full use of existing Internet video-streaming resources like RTP players and servers. In this paper, we propose the converter model to solve this compatibility issue. Based on this model, we first modify the RTP using the erasure coding (EC) technique to address the high packet loss rate of VANETs. This protocol is known as EC-RTP. Then, we develop two converters; the first stands on the boundary between the Internet and VANETs. It receives the RTP packets from the Internet and translates them to EC-RTP packets, which are shared between vehicles. The second converter receives EC-RTP packets, translates them back to RTP packets, and then sends them to the RTP player.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".