Channel quality-based AMC and smart scheduling scheme for SVC video transmission in LTE MBSFN networks
Bibliographic record
Abstract
Mobile TV and Video on Demand (VoD) streaming services represents an important service which will be provided by Fourth Generation (4G) cellular networks. Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) is one of the most successful 4G technologies used by most of the 4G operators for delivering mobile broadband. In the past, cellular systems have mostly focused on unicast transmission systems. But, with the adoption of new services which are intended to be delivered to a broad range of users, multicast and broadcast systems are becoming widely popular. Enhanced Multimedia Broadcast/Multicast Service (EMBMS) is defined in 3GPP specification to support download delivery and streaming delivery to group users in LTE mobile networks. In 3GPP Release 8 specification, the EMBMS transmission is classified into single-cell transmission and MBSFN (Multicast Broadcast Single Frequency Network) transmission. H.264 was the recommended video codec for Universal Mobile Telecommunication System (UMTS) MBMS service. However, the Scalable Video Coding (SVC) extension of H.264 allows efficient temporal, spatial and quality scalabilities. In this paper, we propose a video streaming method with SVC for MBSFN networks with adaptive modulation and coding (AMC) and frequency scheduling based on distribution of users in different channel quality regions. Through simulations we demonstrate that spectrum savings in the order of 72 to 82% is achievable in different user distribution scenarios with our proposed scheme. These savings in spectrum can be used for serving other MBSFN, single cell MBMS or unicast bearers, and it can also be used for increasing the video quality of the same MBSFN bearer.
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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 | 0.001 | 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".