Analyzing the effect of LTE-A transmission parameters on video streaming quality of experience
Why this work is in the frame
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Bibliographic record
Abstract
Cellular networks have witnessed an increasing demand for video streaming applications recently, and this is expected to further increase in the upcoming years. Providing high Quality of Experience (QoE) video streaming services is becoming a challenge for cellular network operators. This is due to the limited capacity in cellular networks and the impairments of transmission over radio links (e.g., path-loss and fading). As such, the parameters of the wireless communication on the radio access network between the Base-Station (BS) and User Equipments (UEs) have an effect on video streaming QoE. We study the impact of the wireless transmission parameters in Long Term Evolution-Advanced (LTE-A) networks on video streaming QoE. We consider both cell level and link level parameters. Dynamic Adaptive Streaming over HTTP (DASH) -based video streaming is considered here. We built a model for an LTE-A network and ran multiple simulations with various scenarios. We present and analyze the results to evaluate different video streaming QoE metrics, and to see how they are affected by the various cellular communication parameters.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it