1 Optimal Bit Allocation for Fine-Grained Scalable Video Sequences in Distributed Streaming Environments
Bibliographic record
Abstract
We present optimal schemes for allocating bits of fine-grained scalable video sequences among multiple senders streaming to a single receiver. The senders are heterogeneous in their outgoing bandwidth and they hold different portions of the video stream. We first formulate and optimally solve the problem for individual frames, then we generalize to the multiple frame case. Specifically, we formulate the allocation problem as an optimization problem with an objective function to minimize the distortion. By using the piece-wise linear rate-distortion model, we transform the general (nonlinear) optimization problem into an integer linear programming (ILP) problem. We further design a simple rounding scheme that transforms the ILP problem into a linear programming (LP) one, which could be solved efficiently using common optimization techniques such as the Simplex method. We prove that our rounding scheme always produces a feasible solution, and the solution is within a negligible margin from the optimal one. We also propose a new algorithm (FGSAssign) for the single-frame allocation problem that runs in O(n log n) steps, where n is the number of senders. We prove that FGSAssign is optimal. Furthermore, we propose a heuristic algorithm (mFGSAssign) that produces near-optimal solutions for the multipleframe case, and runs an order of magnitude faster than the optimal one. Our extensive experimental study shows the effectiveness of our allocation algorithms in improving the video playout quality. 2 I.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".