Scalable Modulation for Video Transmission in Wireless Networks
Bibliographic record
Abstract
Abstract—In conventional wireless systems with layered architectures, the physical (PHY) layer equally treats all data streams from the upper layers and applies the same modulation and coding schemes to them. Newer systems such as Digital Video Broadcast start to introduce hierarchical modulation schemes with SuperPosition Coding (SPC) and support data streams of different priorities. However, SPC requires specialized hardware and has high complexity, which is not desirable for handheld devices. In this paper, we propose scalable modulation (s-mod) by reusing the current mainstream modulation schemes with software-based bit remapping. The performance evaluation has shown that s-mod can achieve the same and, in some cases, even better performance than SPC with much lower complexity. We further propose how to optimize the configuration of the PHY-layer s-mod and coding schemes to maximize the utility of video streaming with scalable video coding (SVC). Simulation results demonstrate substantial performance gains using s-mod and cross-layer optimization, indicating that s-mod and SVC are a good combination for video transmission in wireless networks. Index Terms—Scalable modulation (s-mod) and coding, scalable video coding (SVC), SuperPosition Coding (SPC). 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".