BIER-MC: Multi-Connectivity Approach for Latency-Efficient Multicast Routing
Bibliographic record
Abstract
Integrating terrestrial networks together with non-terrestrial networks, such as Low Earth Orbit (LEO) satellites, can significantly enhance network reliability and reduce latency in unicast and multicast protocols. To use such a capability and reduce end-to-end latency, many transport-layer unicast protocols, such as Multipath TCP (MPTCP) and Multipath QUIC (MPQUIC), have adopted multi-connectivity (MC) by transmitting over multiple interfaces at the sender host. However, traditional and modern multicast protocols suppose that the sender host accesses the core network using a single path, leading to high end-to-end latency at destinations. In this regard, we first demonstrate in a real-world testbed that MC significantly improves network latency in multicasting compared to single-connectivity approaches. We then introduce a Bit Indexed Explicit Replication (BIER) Multi-Connectivity (BIER-MC) method designed to reduce end-to-end latency within the BIER protocol, as a modern multicast protocol. Our comparison indicates that BIER-MC outperforms traditional BIER implementations using multicast trees in terms of latency up to 5×, bandwidth usage up to 2.5×, and edge betweenness centrality up to 15×.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".