A real-time video multicast architecture for assured forwarding services
Bibliographic record
Abstract
This paper presents our work on developing an architecture for multicasting real-time MPEG4 over IP networks that provide service differentiation. In particular, this work is targeted at assured forwarding (AF) style services. This work is an attempt to find a simple solution to the problem of multicast congestion control of real-time traffic by exploiting the service differentiation capabilities of AF networks. Our architecture assumes loss differentiation in the network and assumes the network's ability to provide explicit congestion notification messages to the sender. We do not consider policing/shaping at the edge routers. Rather, we consider a more general case where packet marking and flow control are provided at the senders. For this network model, we built an end-to-end architecture and developed a rate-adaptation algorithm that can operate in both unicast and multicast applications with a minor modification. The simulation results show how the rate-adaptation algorithm accommodates different receivers with different networking capabilities and provides receivers with different levels of quality by taking advantage of the queue management capabilities of the AF service. We test how the architecture scales to a large number of receivers, how multiple multicast sessions interact, and how it interacts with TCP.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".