Analyzing the effect of LTE-A transmission parameters on video streaming quality of experience
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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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".