Performance evaluation of video streaming over multi-hop wireless local area networks
Bibliographic record
Abstract
IEEE 802.11 WLAN is preferred for IPTV in-home distribution, but the achievable throughput and coverage are still limited due to the high attenuation and interference in a household environment. Through our measurement study with a WDS-based multi-hop wireless testbed, we have found that it is possible for multi-hop wireless networks to increase the coverage and improve the video streaming performance at the same time. To analyze the throughput of IEEE 802.11 multihop wireless networks, we propose an extended two-dimensional Markov-chain model in this paper. Different from existing work, our model takes the retry limit and post-backoff stage into account to better capture the behavior of IEEE 802.11 MAC protocols in a non-ideal channel and with non-persistent traffic. The throughput analysis is validated by network simulation with extended lower and upper-layer simulation modules. The achievable throughput gives an upper bound of the video streaming performance, which is further validated by our H.264-based video streaming simulation with application-layer performance metrics. The results correspond to the observation we had on the multi-hop testbed. Further, this paper also provides some guidance on how to achieve the optimal balance for a given scenario, which is important when deploying video streaming services with end-to-end quality-of-service provisioning.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".