A survey of congestion control schemes for multicast video applications
Bibliographic record
Abstract
Congestion control for IP multicast on the Internet has been one of the main issues that challenge a rapid deployment of IP multicast. In this article, we survey and discuss the most important congestion control schemes for multicast video applications on the Internet. We start with a discussion of the different elements of a multicast congestion control architecture. A congestion control scheme for multicast video possesses specific requirements for these elements. These requirements are discussed, along with the evaluation criteria for the performance of multicast video. We categorize the schemes we present into end-to-end schemes and router-supported schemes. We start with the end-to-end category and discuss several examples of both single-rate multicast applications and layered multicast applications. For the router-supported category, we first present single-rate schemes that utilize filtering of multicast packets by the routers. Next we discuss receiver-based layered schemes that rely on routers group/flow control of multicast sessions. We evaluate a number of schemes that belong to each of the two categories.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".