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Record W1607463541 · doi:10.1109/iccw.2015.7247177

Handling real-time video traffic in software-defined radio access networks

2015· article· en· W1607463541 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
TopicVideo Coding and Compression Technologies
Canadian institutionsHuawei Technologies (Canada)
Fundersnot available
KeywordsComputer scienceReal-time computingVideo qualityComputer networkVideoconferencingSoftware-defined radioSoftwareMultimediaMetric (unit)TelecommunicationsOperating system

Abstract

fetched live from OpenAlex

In this paper, we introduce new solutions to handle real-time video traffic, such as video conferencing, in the future software-defined radio access networks. The real-time video is well-known for its high peak-to-mean rate ratio. This is a major challenge for traffic engineering and radio resource allocation, especially in small cell radio networks. We first propose an online method to dynamically estimate the effective rate of video flows, which is the rate the network should support in order to provide a satisfactory quality of experience. Second, traffic engineering methods taking into account characteristics of video flows are presented. Third, a radio coordination method to provide stable video rate across cells is discussed. Fourth, we give a fountain coding scheme to support mobile video users. The proposed solutions are investigated in an ultra-dense small cell network simulator. The simulation results show very significant gains over conventional technologies.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.773
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.040
GPT teacher head0.273
Teacher spread0.233 · 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

Citations13
Published2015
Admission routes1
Has abstractyes

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