Structuring Multi-Layer Scalable Streams to Maximize Cient-Perceived Quality
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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