Towards end-to-end loss guarantees for streaming video in a multi-hop IP network
Bibliographic record
Abstract
Video streaming over IP networks requires end-to-end loss guarantees. In order to achieve the required end-to-end loss performance, new algorithms for the distribution of end-to-end loss requirements into local loss constraints, as well as provision of local loss assurance, are proposed. Much of recent research pays little attention to the problem of distributing end-to-end loss requirements to local routing nodes. This paper proposes five schemes for the allocation of nodal loss across a multi-hop IP network. In addition, it uses loss-aware packet scheduling to provide nodal loss assurance, rather than using buffer management as in present node-based loss guarantee techniques. Simulation experiment results demonstrate that integrating the proposed loss allocation methods with the loss-aware packet scheduling scheme not only provides end-to-end loss guarantee for each video, but also greatly improves fairness in quality of service amongst videos.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".