Exploiting cluster multicast for P2P streaming application in cellular system
Bibliographic record
Abstract
Streaming over peer-to-peer (P2P) network is popular, however causes needless traffic traversal through multiple links due to the mismatch between the physical and the overlay network. Cellular channels are limited in number and expensive. Because of the magnitude of contents per unit time and the nature of playing same contents throughout the entire system, collaborative streaming approach is the key to an efficient P2P streaming system. In this paper, we propose a collaborative streaming system where some cellular peers download contents from the Internet peers, and then share the contents with the remaining cellular peers by employing device-to-device (D2D) multicast application in order to avoid bottleneck at the eNodeB (eNB), and to reduce streaming cost. We present the broadcasters/agents and their optimal assisted peers selection problem as stable admission assignment and formulate the problem as integer linear programming (ILP) problem. We also present a distributed algorithm to select agents among the cellular peers with suitable number of assisted peers for each agent to tackle the retransmission problem. The cellular peers change role as a broadcaster or a multicast receiver to ensure fairness. We also perform extensive simulation to show the efficacy of our design and to verify our claims.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".