Structuring Multi-Layer Scalable Streams to Maximize Cient-Perceived Quality
Bibliographic record
Abstract
Video coders, such as H.264/SVC, can encode a video stream into multiple layers, each with a different rate. Moreover, each layer can either be coarse-grained scalable (CGS) or fine-grained scalable (FGS). FGS layers support wider ranges of client bandwidth than CGS layers, but suffer from higher coding inefficiency. Currently there are no systematic ways in the literature to determine the optimal stream structure that renders the best average quality for all clients. In this paper, we formulate an optimization problem to determine the optimal rate and encoding granularity (CGS or FGS) of each layer in a scalable video stream that maximizes a system-defined utility function for a given client distribution. We design an efficient, yet optimal, algorithm to solve this optimization problem. Our algorithm is general in the sense that it can employ arbitrary utility functions for clients. We implement our algorithm and verify its optimality. We show how various structuring of scalable video streams affect individual client utilities. We compare our algorithm against a heuristic algorithm that has been used before in the literature, and we show that our algorithm outperforms the other one in all cases.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".