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Record W2158424206 · doi:10.1109/icc.1995.524442

Rate control of VBR H.261 video on frame relay networks

2002· article· en· W2158424206 on OpenAlexaff
C.M. Sharon, Michael Devetsikiotis, Ioannis Lambadaris, A.R. Kaye

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsCarleton University
FundersInstituto de Telecomunicações
KeywordsVariable bitrateComputer scienceCodecReal-time computingComputer networkQuantization (signal processing)Frame RelayNetwork congestionQuality of serviceConstant bitrateFrame (networking)AlgorithmComputer hardwareNetwork packet

Abstract

fetched live from OpenAlex

The H.261 video compression standard provides an efficient scheme for the transmission of low bit-rate video traffic over high-speed networks. We introduce a modified H.261 codec that produces variable bit-rate (VBR) output for transmission over an integrated services frame relay (FR) network. We show that the quality of service requirements of VBR video can be met in the presence of inter-LAN data traffic by making use of the backward explicit congestion notification (BECN) facility, in conjunction with a modified H.261 video codec whose rate is controlled by the congestion notifications. We also show that the performance of the control mechanism is significantly influenced by a subset of network threshold and codec control parameters which is identified using 2/sup k/ factorial analysis techniques. We obtain optimal ranges of values for these parameters using mean field annealing optimization methods. Finally, we show that variable quantization rate control is more effective for this purpose than variable frame rate control and that the improvement in performance over that of an uncontrolled network is significant.

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)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.172
Teacher spread0.166 · 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

Citations15
Published2002
Admission routes1
Has abstractyes

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