H.265 video capacity over beyond-4G networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| 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 it