DASH-based Device-to-Device Video Streaming for Cellular Networks with High User Density
Bibliographic record
Abstract
Supporting video streaming services and providing high Quality of Experience (QoE) to end users have become main concerns for cellular network operators. In this work, we present an architecture for improving the QoE of video streaming in cellular networks with high user density. The architecture employs progressive caching of video contents, Dynamic Adaptive Streaming over HTTP (DASH), and Device-to-Device (D2D) communication. The Base-Station (BS) controls the progressive caching process of video contents and the Peer-to-Peer (P2P) transmission of video segments among User Equipments (UEs). We present two different implementations of the architecture. The implementations differ in the employed approach for video contents caching and distribution over the UEs. We use the Discrete EVent System Specification (DEVS) formalism to build a model for the proposed architecture in an LTE-A network and use the model to study the performance achieved by the proposed architecture in terms of many video streaming QoE metrics.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".