Spectrum Access Quality for Mobile Broadband Video Communications in Smart Cities
Bibliographic record
Abstract
Cities use broadband wireless spectrum access technologies to enable a wide range of smart city applications that enhance safety and security, improve efficiency of municipal services and promote a better quality of life for residents and visitors. The most quality and bandwidth-demanding smart city scenarios usually involve video communications. New compression technologies have made the delivery of high-resolution video over access networks a reality. However, video delivery must take into account network characteristics in different environmental conditions. This paper studies the possibility of providing high resolution video in mobile environments over the downlink and uplink of a broadband wireless access network, without compromising the video quality. The downlink scenario typically corresponds to offering multicast/unicast video to mobile residents and visitors, whereas the uplink scenario can be for video surveillance for public safety and electronic news gathering. For this study, measurements were obtained using professional video streaming equipment, on a commercially available broadband wireless access system in a typical emulated mobile environment. Analysis was done for different video settings and spectrum and network configurations in order to characterize network performance and assess video quality in different conditions. A key outcome of this analysis was to determine the feasibility of high resolution video delivery over broadband wireless access networks in mobile conditions and to point recommendations for the QoE optimization of mobile video reception. A good quality of experience is possible provided that system and environment limitations are respected.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".