Scaling laws and tradeoffs in peer-to-peer live multimedia streaming
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is well-known that live multimedia streaming applications operate more efficiently when organized in peer-to-peer (P2P) topologies, since peer upload capacities are utilized to support other peers, and to alleviate the load and operating costs on the streaming servers. To date, there have been a number of existing experimental proposals with respect to how such peer-to-peer topologies are organized to support live streaming sessions. However, most of the existing proposals resort to intuition and heuristics when it comes to the design of such topology construction (i.e., neighbor selection) protocols. In this paper, we investigate the scaling laws of live P2P multimedia streaming, by quantitatively studying the asymptotic effects and tradeoffs among three key parameters in P2P streaming: server bandwidth cost, the maximum number of peers that can be supported, and the maximum number of streaming hops experienced by a peer. To further generalize our studies, we do not make restrictive assumptions in our theoretical analysis of such scaling laws: both peer upload capacities and peer lifetimes in a session may come from arbitrary distributions. With the theoretical insights we have developed, we propose Affinity, a simple and realistic heuristic to demonstrate the key benefits of our theoretical analysis in dynamic P2P networks, as compared to the topology construction algorithms in existing work.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it