Energy-efficient multicasting of multiview 3D videos to mobile devices
Bibliographic record
Abstract
Multicasting multiple video streams over wireless broadband access networks enables the delivery of multimedia content to large-scale user communities in a cost-efficient manner. Three dimensional (3D) videos are the next natural step in the evolution of digital media technologies. In order to provide 3D perception, 3D video streams contain one or more views that greatly increase their bandwidth requirements. Due to the limited channel capacity and variable bit rate of the videos, multicasting multiple 3D videos over wireless broadband networks is a challenging problem. In this article, we consider a 4G wireless access network in which a number of 3D videos represented in two-view plus depth format and encoded using scalable video coders are multicast. We formulate the optimal 3D video multicasting problem to maximize the quality of rendered virtual views on the receivers' displays. We show that this problem is NP-complete and present a polynomial time approximation algorithm to solve it. We then extend the proposed algorithm to efficiently schedule the transmission of the chosen substreams from each video in order to maximize the power saving on the mobile receivers. Our simulation-based experimental results show that our algorithm provides solutions that are within 0.3 dB of the optimal solutions while satisfying real-time requirements of multicast systems. In addition, our algorithm results in an average power consumption reduction of 86%.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".