An optimized link adaptation scheme for efficient delivery of scalable H.264 Video over IEEE 802.11n
Bibliographic record
Abstract
In this paper, we propose a cross-layer optimization scheme for delivery of scalable video over variable bit-rate wireless networks, in particular 802.11 based wireless local area networks (WLAN). For scalable video streaming applications, the conventional solution to reduced throughput due to channel distortions is to reduce the video bitrate by dropping the higher enhancement layers of the scalable video. We show that video quality can be improved, without adding to traffic load, when the WLAN link adaptation scheme uses a temporal fairness criterion along with scalable video distortion estimates to adjust its physical (PHY) layer modulation and coding parameters used for delivering each video layer. We formulate the problem as an optimization problem for assigning different PHY modes to different layers of scalable video under temporal fairness constrains; the solution to this problem provides a set of PHY configuration parameters that achieve the highest possible video quality while meeting the admission control constraints. Performance evaluations demonstrate the effectiveness of our method and the accuracy of the models.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".