Using simulcast and scalable video coding to efficiently control channel switching delay in mobile tv broadcast networks
Bibliographic record
Abstract
Many mobile TV standards dictate using energy saving schemes to increase the viewing time on mobile devices, since mobile receivers are battery powered. The most common scheme for saving energy is to make the base station broadcast the video data of a TV channel in bursts with a bit-rate much higher than the encoding rate of the video stream, which enables mobile devices to turn off their radio frequency circuits when not receiving bursts. Broadcasting TV channels in bursts, however, increases channel switching delay. The switching delay is important, because long and variable switching delays are annoying to users and may turn them away from the mobile TV service. In this article, we first analyze the burst broadcasting scheme currently used in many deployed mobile TV networks, and we show that it is not efficient in terms of controlling the channel switching delay. We then propose new schemes to guarantee that a given maximum switching delay is not exceeded and that the energy consumption of mobile devices is minimized. We prove the correctness of the proposed schemes and analytically analyze the achieved energy saving. We also use scalable video coding to generalize the proposed schemes in order to support mobile devices with heterogeneous resources. We implement the proposed schemes in a mobile TV testbed to show their practicality and to validate our theoretical analysis. The experimental results show that the proposed schemes: (i) significantly increase the energy saving achieved on mobile devices: up to 95% saving is observed, and (ii) support both homogeneous and heterogeneous mobile devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".