Bottom-up trie structure for P2P live streaming
Bibliographic record
Abstract
By simultaneously providing live video and audio contents to millions of users around the world, peer-to-peer live video streaming (P2P LVS) has become one of the most popular Internet applications in recent years. However, current P2P LVS software has problems such as non-smooth playback and long start-up delay for end users. To address these issues, we design a P2P-based multi-bit Trie structure, called Bottom-Up Trie (BU-Trie), for distributing P2P live contents. Different from other approaches, BU-Trie is a Trie formed and built inversely from leaf nodes (or child nodes) back to the root node (or parent node). This architecture consists of two phases: a diffusion phase and a swarming phase. The main design goal of the diffusion phase is to group the local peers together by discovering physical locations of peers, and design the paths for fast distributing live streams from the source node to end users. The objective of the swarming phase is to find an optimal way for exchanging the video stream chunks within a local group. We propose an algorithm called Most Popular Chunk First (MPCF) and apply it for the swarming phase for efficient chunk exchange. Performance evaluation of the proposed BU-Trie shows that, when compared to other approaches, the sequential throughput of video chunks is increased. The inter-domain traffic, the traffic between different Internet service providers (ISPs), is reduced as well. Such a reduction would benefit carriers economically.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".