Flexible Broadcasting of Scalable Video Streams to Heterogeneous Mobile Devices
Bibliographic record
Abstract
We study the scalable video broadcasting problem in mobile TV broadcast networks, where each TV channel is encoded into a scalable video stream with multiple layers, and several TV channels are concurrently broadcast over a shared air medium to many mobile devices with heterogeneous resources. Our goal is to encapsulate and broadcast video streams encoded in scalable manner to enable heterogeneous mobile devices to render the most appropriate video substreams while achieving high energy saving and low channel switching delay. The appropriate streams depend on the device capability and the target energy consumption level. We propose two new broadcast schemes, which are flexible in the sense that they allow diverse bit rates among layers of the same stream. Such flexibility enables videos to be optimally encoded in terms of coding efficiency, and allows the coded video streams to be better matched with the capability of mobile devices. We analyze the performance of the proposed broadcast schemes. In addition, we have implemented the proposed schemes in a real mobile TV testbed to show their practicality and efficiency. Our extensive experiments confirm that the proposed schemes enable energy saving differentiation: between 75 and 95 percent were observed. Moreover, one of the schemes achieves low channel switching delays: 200 msec is possible with typical system parameters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".