Bibliographic record
Abstract
Long Term Evolution (LTE) has been standardized by the 3GPP consortium since 2008 in 3GPP Release 8, with 3GPP Release 12 being the latest iteration of LTE Advanced (LTE-A), which was finalized in March 2015. High Efficiency Video Coding (H.265) has been standardized by MPEG since 2012 and is the Video Compression technology targeted to deliver High-Definition (HD) and Ultra High-Definition (UHD) Video Content to users. With video traffic projected to represent the lion's share of mobile data traffic, providing users with high Quality of Experience (QoE) is key to designing 4G systems and future 5G systems. In this paper, we present a cross-layer scheduling framework which delivers frames to unicast video users by exploiting the encoding features of H.265. We extract information on frame references within the coded video bitstream to determine which frames have higher utility for the H.265 decoder located at the user's device and evaluate the performances of best-effort and video users in 4G networks using finite buffer traffic models. Our results demonstrate that there is significant potential to improve the QoE of all users compared to the baseline Proportional Fair method by adding media-awareness in the scheduling entity at the Medium Access Control (MAC) layer of a Radio Access Network (RAN).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".