Time Slicing in Mobile TV Broadcast Networks with Arbitrary Channel Bit Rates
Bibliographic record
Abstract
Mobile TV networks have received significant attention from the industry and academia, as they have already been deployed in several countries and their expected market potential is huge. In such networks, a base station broadcasts TV channels in bursts with bit rates much higher than the encoding bit rates of the videos. This enables mobile receivers to receive a burst of traffic and then turn off their receiving circuit till the next burst to conserve energy. The base station needs to construct a transmission schedule for all bursts of different TV channels. Constructing optimal (in terms of energy saving) transmission schedules has been shown to be an NP-complete problem when the TV channels are encoded at arbitrary bit rates. In this paper, we propose a near-optimal approximation algorithm to solve this problem. We prove the correctness of the proposed algorithm and derive its approximation factor. We also conduct extensive evaluation of our algorithm using real implementation in a mobile TV testbed and simulations. Our experimental and simulation results show that the proposed algorithm: (i) is practical and produces correct burst schedules, (ii) achieves near-optimal energy saving for mobile devices, and (iii) runs efficiently in real time.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".