Cross-layer optimization of video streaming in single-hop wireless networks
Bibliographic record
Abstract
Video streaming over wireless networks is getting very popular because of the high bandwidth and the support of quality of service offered by recent wireless standards, such as IEEE 802.11e. We consider optimizing the quality of video streaming in single-hop wireless networks that are composed of multiple wireless stations. Our optimization problem controls parameters in different layers to optimally allocate the wireless network resources among all stations. We address this problem in two steps. First, we formulate an abstract optimization problem for video streaming in single-hop wireless networks in general. This formulation exposes the important interaction between parameters belonging to different layers in the network stack. Then, we instantiate and solve the general problem for the recent IEEE 802.11e WLANs, which support prioritized traffic classes. We show how the calculated optimal solutions can efficiently be implemented in the distributed mode of the IEEE 802.11e standard. We evaluate our proposed solution using extensive simulations in the OPNET simulator, which captures most features of realistic wireless networks. In addition, to show the practicability of our solution, we have implemented it in the driver of an off-the-shelf wireless adapter that complies with the IEEE 802.11e standard. Our experimental and simulation results show that significant quality improvement in video streams can be achieved using our solution, without incurring any significant communication or computational overhead.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".