A novel differentiated retransmission scheme for MPEG video streaming over wireless links
Bibliographic record
Abstract
With the dramatic improvement on scalability and flexibility of the MPEG-4 standard, video-based services are expected to become one of the most important bandwidth consumers in the next-generation wireless networks. However, providing a quality video delivery over wireless networks poses many challenges due to a high Bit Error Rate (BER), time-varying characteristics of wireless channels and a stringent delay and delay jitter requirement for real-time applications. To solve these problems, a suite of interoperable approaches must be devised for enhancing the robustness of video streaming to the error-prone environment. In this paper, we propose a Differentiated Automatic Repeat Request (DARQ) scheme for MPEG video streaming over wireless links, in which the inter-frame dependency and error propagation are jointly considered and a specific retransmission attempt is assigned to each frame in a Group of Pictures (GOP) according to its significance in the reconstruction of the video at the end-user. Both analytical modelling and extensive simulations have been conducted to verify the proposed scheme. The results demonstrate that the playable frame rate can be substantially improved by using the DARQ scheme compared with that by ARQ schemes employing the uniform retransmission persistency for all frames or assigning the retransmission persistency only depending on the frame type.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".