Three-dimensional absorbing Markov chain model for video streaming over IEEE 802.11 wireless networks
Bibliographic record
Abstract
The varying wireless channel conditions necessitate the use of error control mechanisms for reliable transmission of video streaming applications. Forward error correction (FEC) and automatic repeat request (ARQ) mechanisms are used at the data-link layer of IEEE 802.11 based wireless networks to avoid and recover from the channel errors. In this paper, a three-dimensional absorbing Markov chain model is presented to accurately calculate the packet transmission time when both the FEC and ARQ mechanisms are used. Based on the calculated packet transmission time and given maximum number of transmission attempts, the number of redundant FEC packets is adjusted to achieve an optimum tradeoff between network overhead and delay. Numerical results show that the three-dimensional absorbing Markov chain model accurately captures the packet delivery dynamics for a given maximum number of transmission attempts at the data-link layer. With the knowledge of accurate packet transmission time, the FEC parameter is adjusted to achieve higher quality video at the terminal devices. The adjustment of the number of FEC packets based on the proposed three-dimensional model brings the combined advantages of reduced network overhead and enhanced video quality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".