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Record W2136823104 · doi:10.1109/glocom.2010.5683763

Video Streaming with PCA and Hard vs Soft DRP

2010· article· en· W2136823104 on OpenAlexafffund
Rukhsana Ruby, Jianping Pan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Victoria
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceComputer networkWirelessQuality of serviceReservationProtocol (science)Real-time computingTelecommunications

Abstract

fetched live from OpenAlex

Video streaming over wireless links is a challenging issue due to the stringent quality-of-service requirement of video traffic and the contention nature of wireless media with limited channel resources. Various wireless Media Access Control (MAC) protocols have been proposed, and most of them follow either the contention-based or contention-free approach. In this paper, we propose to stream video traffic over both contention-based and contention-free MAC protocols, exampled by WiMedia UWB Prioritized Contention Access (PCA) and Distributed Reservation Protocol (DRP), and then evaluate the performance of such an approach with extensive simulation. The novelty of our approach is to use both PCA and DRP for every single video stream by reserving well below the peak data rate and handling traffic burst through reduced contention. The simulation results further reveal the tradeoff between hard and soft DRP with single or dual buffer on the performance of video streaming.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2010
Admission routes2
Has abstractyes

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