Enhanced TCP-friendly rate control for supporting video traffic over internet
Bibliographic record
Abstract
Video traffic nowadays forms the majority of traffic over the Internet, and is predicted to be the most prevailing traffic type in the coming few years. TCP-Friendly Rate Control (TFRC) is one of the most promising end-to-end congestion control protocols that is intended for unicast playback of Internet streaming applications. This paper presents a new TCP-Friendly congestion control protocol, called Enhanced TCP-Friendly Rate Control (ETFRC), for supporting real time video traffic over the Internet. The proposed protocol is developed by adjusting the sending rate, at the sender side, dynamically based on the current state of the network, and the current state of the receiver. In other words, ETFRC embodies a new algorithm to tune (increase or decrease) the sending rate, at the sender side, according to the difference between the calculated rate by the sender and the reported rate from the receiver side. The performance of the proposed ETFRC protocol is evaluated using the network simulator ns-2 considering different scenarios. In these scenarios, simulated video traffic from the Evalvid framework is sent over the designed topology and different performance parameters are measured and compared with that obtained by applying the original TFRC protocol. The simulation results show that ETFRC performance surpassed TFRC in terms of throughput, jitter, and packet loss.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".