Adaptive video protection in large scale peer‐to‐peer video streaming over mobile wireless mesh networks
Bibliographic record
Abstract
Summary Because video streaming over mobile handheld devices has been of great interest, the necessity of introducing new methods with low implementation cost and scalable infrastructures is a strong demand of the service. In particular, these requirements are present in popular wireless networks such as wireless mesh networks (WMN). Peer‐to‐peer (P2P) networks promise an efficient scalable network infrastructure for video streaming over wired and wireless networks. Limited resources of the peers in P2P networks and high error rate in wireless channels make it more challenging to run P2P streaming applications over WMNs. Therefore, it is necessary to design efficient and improved error protection methods in P2P video streaming applications over WMNs. In this paper, we propose a new adaptive unequal video protection method specially intended for large scale P2P video streaming over mobile WMNs. Using this method, different frames have different priorities in receivers along the recovery process. Moreover, we precisely and completely evaluate different aspects related to frame protection in these networks using five important performance metrics including video distortion, late arrival distortion, end‐to‐end delay, overhead and initial start‐up delay. The results obtained from a precise simulation in OMNeT++ show that the proposed adaptive method significantly outperforms other solutions by providing better video quality on mobile wireless nodes. Copyright © 2015 John Wiley & Sons, Ltd.
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 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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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 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".