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Record W1965009150 · doi:10.1109/glocom.2012.6503401

QoE-driven cache management for HTTP adaptive bit rate (ABR) streaming over wireless networks

2012· article· en· W1965009150 on OpenAlex
Weiwen Zhang, Yonggang Wen, Zhenzhong Chen, Ashish Khisti

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 Toronto
Fundersnot available
KeywordsComputer scienceCacheWireless networkWirelessBit rateReal Time Streaming ProtocolComputer networkQuality of experienceQuality of serviceOperating system

Abstract

fetched live from OpenAlex

In this paper, we investigate the problem of how to cache a set of media files with optimal streaming rates, under HTTP adaptive bit rate streaming over wireless networks. The design objective is to achieve the optimal expected QoE under a limited storage budget, which is measured by the logarithmic relation between the required bit rate and the actual streaming bit rate. We formulate the content cache management of streaming files as a constrained optimization problem. Lagrange multiplier method is employed, and we obtain the numerical solution of the optimal streaming files. Particularly, we characterize the properties of the solution, and find there is a fundamental phase change in the optimal solution as the number of cached files grows. Moreover, the simulation results indicate that with the increase of cache size, more copies of different bit rate should be cached for a better QoE. Our comprehensive investigation reveals insightful guidelines to provide HTTP ABR streaming services over wireless networks.

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 score0.964
Threshold uncertainty score0.687

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 communication0.0000.001
Open science0.0010.000
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 head0.036
GPT teacher head0.301
Teacher spread0.265 · 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

Citations14
Published2012
Admission routes1
Has abstractyes

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