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Record W2172007097 · doi:10.1109/tcsvt.2010.2077553

Solving Streaming Capacity Problems in P2P VoD Systems

2010· article· en· W2172007097 on OpenAlex
Yifeng He, Ling Guan

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Circuits and Systems for Video Technology · 2010
Typearticle
Languageen
FieldComputer Science
TopicPeer-to-Peer Network Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceUploadLive streamingBandwidth (computing)Video on demandComputer networkPeer-to-peerReal Time Streaming ProtocolResource allocationThe InternetVideo streamingWorld Wide Web

Abstract

fetched live from OpenAlex

Peer-to-peer (P2P) video-on-demand (VoD) is a popular Internet service for a large number of concurrent users. Streaming capacity in a P2P VoD system is defined as the maximum streaming rate that can be received by every user. In this letter, we study the streaming capacity problem in P2P VoD systems. We formulate the streaming capacity problem into an optimization problem which maximizes the streaming rate subject to peer bandwidth constraints, and then solve it with a distributed algorithm. From the study on streaming capacity, we find that the streaming capacity is limited by the over-demanded video segments. Therefore we introduce helpers, the peers who are willing to contribute their remaining upload bandwidths to help other peers, into P2P VoD systems. We optimize helper assignment and rate allocation to improve the streaming capacity. In the simulations, we demonstrate that the streaming capacity can be obtained in a distributed manner by optimizing the resource allocation in the P2P VoD system.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.788
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.238
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it