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Record W2036152326 · doi:10.1145/2736084.2736090

A fuzzy-based rate adaptation controller for DASH

2015· article· en· W2036152326 on OpenAlex

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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicImage and Video Quality Assessment
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceFuzzy logicReal-time computingDashThroughputDynamic Adaptive Streaming over HTTPVariable bitrateScheduling (production processes)Bandwidth (computing)Controller (irrigation)Fuzzy control systemControl theory (sociology)Computer networkBit rateArtificial intelligenceControl (management)Quality of serviceEngineeringOperating systemQuality of experience

Abstract

fetched live from OpenAlex

As dynamic delivery of video over HTTP becomes prominent, rate adaptation techniques become more challenging due to bandwidth variations. This paper presents a Fuzzy-based controller to dynamically adapt the video bitrate based on both the estimated throughput and the size of the playback buffer. The proposed Fuzzy Logic Controller (FLC) mechanism takes the observed throughput and buffer dynamics as inputs to change the policy of selecting the video bitrate and download scheduling to minimize the negative effect of ON-OFF switching. The experimental results show that our proposed mechanism generates a smoother stream compared to existing methods.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.943
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.124
GPT teacher head0.343
Teacher spread0.219 · 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

Quick stats

Citations20
Published2015
Admission routes1
Has abstractyes

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