An Efficient Video Adaptation Scheme for SVC Transport over LTE Networks
Bibliographic record
Abstract
Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) offers high data rate capabilities to mobile users, and, operators are trying to deliver a true mobile broadband experience over LTE networks. Mobile TV and Video on Demand (VoD) are expected to be the main revenue generators in the near future and efficient video streaming over wireless is the key to enabling this. In this paper, we are proposing an efficient adaptation scheme for Scalable Video Coding (SVC) transport over LTE networks and investigate the benefits of this scheme for video streaming over LTE networks. Video streaming over LTE networks is analyzed using a 3GPP compliant LTE simulator using H.264 and SVC video traces. Analysis is done using real time use cases of mobile video streaming. Different parameters like throughput, packet loss ratio, delay, and jitter are compared with H.264 single layer video for unicast and multicast scenarios using different kinds of scalabilities. Results show that considerable packet loss reduction and throughput savings (18 to 30%) with acceptable video quality are achieved with proposed scheme based on SVC compared to H.264. Advantages of proposed scheme for LTE networks are evident from the simulation results.
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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) | 0.000 | 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 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".