Optimally scheduling video-on-demand to minimize delay when sender and receiver bandwidth may differ
Bibliographic record
Abstract
We establish tight bounds on the intrinsic cost (either minimizing delay d for fixed sender and receiver bandwidths, or minimizing sender bandwidth for fixed delay and receiver bandwidth) of broadcasting a video of length m over a channel of bandwidth S in such a way that a receiver (with bandwidth R ), starting at an arbitrary time s , can download the video so that it can begin playback at time s + d .Our bounds are realized by a simple just-in-time protocol that partitions the video into a fixed number of segments, partitions the sender bandwidth into an equivalent number of equal bandwidth subchannels, and broadcasts each segment repeatedly on its own subchannel. The protocol is suitable for the broadcast of compressed video and it can be implemented so that video information is packaged into discrete fixed length packets incurring only a modest overhead (measured in terms of increased delay).Our primary contribution is a lower bound on the required delay that applies to all protocols. This lower bound matches the behavior of our just-in-time protocol in the limit as the number of segments approaches infinity, provided the video compression satisfies some uniform upper bound. For a fixed number of segments, our protocol is optimal within a broad class of protocols, even if the video is compressed arbitrarily.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".