DASH-based peer-to-peer video streaming in cellular networks
Bibliographic record
Abstract
Cellular networks have increasing demands for video streaming applications recently. This makes it challenging for cellular networks operators to provide streaming services with high Quality of Experience (QoE). Here, we propose a novel architecture for improving the QoE of video streaming in cellular networks. The architecture employs Base-Station (BS) -assisted Peerto- Peer (P2P) video streaming in cellular networks. Furthermore, the architecture employs the Dynamic Adaptive Streaming over HTTP (DASH); an adaptive bit rate video streaming technique. 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. We also use the model to simulate a conventional DASH video streaming over a cellular network, i.e., without P2P streaming. Simulation results show that the proposed architecture achieves significant improvement in terms of video streaming QoE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".