A Hybrid Video Dissemination Protocol for VANETs
Bibliographic record
Abstract
Due to stringent requirements of video streaming and the highly dynamic topology of vehicular networks, the designing of an efficient protocol for disseminating high quality video over VANETs has become extremely challenging. A robust and efficient protocol should guarantee the quality of transmitted videos over a network in terms of Quality of Service (QoS) and Quality of user Experience (QoE). Most existing protocols for video streaming over VANETs focus on one aspect of QoS while overlooking others. Besides this, some of these protocols do not consider QoE; hence in some cases, even a very small percentage of packet loss in video networks could lead to provide unusable service. Thus, the goal of this work is to develop an efficient protocol that integrates various promising techniques in an optimal manner in order to support high quality video streaming by way of considering vehicular network peculiarities. This paper proposes a Hybrid Video Dissemination Protocol (HIVE) that deploys a receiver-based relay node selection technique in addition to a MAC congestion control mechanism; this is carried out to avoid high packet collision and latency in respect to vehicle traffic conditions. A combination of these two techniques in an integrated manner provides reasonably high packet delivery ratios. In addition to this, the applying of the Erasure Coding technique on the application layer enhances HIVE performance further by almost no packet loss. This protocol also outperforms the other existing video streaming protocols for VANETs in terms of reconstructed video quality while it complies with scalability and delay requirements for video streaming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".