Power Efficient High Quality Multimedia Multicast in LTE Wireless Networks
Bibliographic record
Abstract
We examine power-efficient high-quality scalable video streaming in LTE networks through its eMBMS service. We consider scalable video streaming and download services offered by eMBMS service over LTE networks. We propose an effective and practical solution to jointly optimize user experience and power consumption in both UE and eNodeB. To perform power efficient multimedia transmission in LTE networks, we face three key trade-offs: (1) maximizing energy saving vs. minimizing delay, (2) maximizing sleep time vs. minimizing lost packets, (3) maximizing quality of video vs. minimizing unnecessary video transmissions. We provide a balanced solution that addresses the trade-off by including user preference. Our simulation results indicate 5% to 18% improvement in base station power consumption and 13% to 25% improvement in UE power conservation chances. The provided solution also decreases the transmitted data in the network while preserving the user perceived quality of the video.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".