A packet prioritization scheme for 3D-HEVC content transmission over LTE networks
Bibliographic record
Abstract
Long Term Evolution (LTE) has been standardized at the 3GPP since 2008 and targets the delivery of high data rate services with strict quality-of-service (QoS) requirements. It is now the fastest ever growing mobile technology and is gradually becoming the mainstream radio access technology used in cellular networks. The latest video coding standard, High Efficiency Video Coding (HEVC), achieves higher compression rate than its predecessor Advanced Video Coding (AVC) and for the same level of quality uses almost 50% less bandwidth. HEVC is the leading video compression technology that will be used to deliver high-definition (HD) and ultra-high-definition (UHD) video content to users. Extensions of HEVC, such as 3D-HEVC, are now being developed and standardized by MPEG to deliver 3D video content. The current issues with LTE include its lack of awareness regarding the type of packets being transmitted, and their importance to the end user. The aim of this paper is to investigate the performance of 3D-HEVC over LTE networks using metrics such as packet loss ratio and average user throughput. We also propose a cross-layer solution in the form of a packet prioritization scheme to help provide better quality-of-experience (QoE) to users and demonstrate its advantages over a baseline scheme that is not QoE-aware.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".