Scalable multiple description coding and distributed video streaming in 3G mobile communications: Research Articles
Bibliographic record
Abstract
This paper proposes a distributed multimedia delivery mobile network for video streaming in 3rd generation (3G) mobile communications. The joint design of layered coding (LC) and multiple description coding (MDC) is employed to address the bandwidth fluctuations and packet loss problems in the wireless network and to further enhance the error resilience tools in MPEG-4. A new Internet protocol (IP) differentiated services (DiffServ) video marking algorithm is presented to support an unequal error protection of the LC components. Both intra-RAN (radio access network) handoff and inter-RAN handoff procedures are discussed, which provide path diversity to combat streaming video outage due to handoff in the universal mobile telecommunications system (UMTS). Computer simulation results demonstrate that: (1) the newly proposed IP DiffServ video marking algorithm is more suitable for video streaming in an IP mobile network as compared with the previously proposed algorithm, and (2) the proposed handoff procedures have better performance in terms of handoff latency, end-to-end delay and handoff scalability than that in UMTS. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".