Statistical multiplexing of self-similar video streams: simulation study and performance results
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it