Coping with flash crowd in multi-channel live video streaming for peer-to-peer networks
Bibliographic record
Abstract
The current multi-channel P2P video streaming architectures incur performance problems including: (i) A low Quality of Service (QoS) in unpopular channels with few viewers; (ii) The sudden drastic increase in the number of requests for a video at the video release time which is referred to as the flash crowd phenomenon. The flash crowd phenomenon in live streaming poses significant challenges in system design. In a P2P live streaming system, a newcomer expects to watch a live video immediately. This paper presents a novel framework for multichannel P2P live video streaming that provides de-centralized mechanisms for handling flash crowds that includes incentive mechanism, load balancing mechanisms, and cross-channel help among the peers for live video streaming in multi-channel P2P systems. Our simulation results demonstrate that the quality of unpopular channels is improved. Moreover, for flash-crowds the proposed method improves the quality of video by reducing the playback delay, distortion, and reducing the redundant traffic.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.002 | 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 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".