Statistical multiplexing of self-similar video streams: simulation study and performance results
Bibliographic record
Abstract
Achieving statistical gains when multiplexing video streams, as in a video-on-demand (VOD) scenario, is difficult because of the stringent QOS demands and the self-similar nature of the traffic. This paper explores, through empirical simulation, the QOS, network utilization, and statistical characteristics of the aggregate traffic resulting from multiple independent MPEG video streams. In addition, the simulation results are compared against several recently-derived theoretical results for self-similar network traffic. Three main results are evident from our experiments. First, moderate statistical multiplexing gain can be achieved when multiplexing multiple self-similar streams. Second, video multiplexing is extremely sensitive to traffic phasing effects and to heavy-tailed frame size distributions. Finally, the theoretical approach considered (Norros (see IEEE Journal on Selected Areas in Communications, vol.13, no.6, p.953-62, 1995) effective bandwidth formulation) appears promising but requires fine-tuning to be practical for call admission and network dimensioning.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".