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Record W1518741822 · doi:10.1109/iccnc.2015.7069438

QoE-aware adaptive bitrate video streaming over mobile networks with caching proxy

2015· article· en· W1518741822 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

Venue2015 International Conference on Computing, Networking and Communications (ICNC) · 2015
Typearticle
Languageen
FieldComputer Science
TopicImage and Video Quality Assessment
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsComputer scienceQuality of experienceConstant bitrateComputer networkQuality of serviceProxy (statistics)Adaptation (eye)Video qualityBandwidth (computing)Dynamic Adaptive Streaming over HTTPWireless networkVideo streamingMultimediaWirelessReal-time computingVariable bitrateTelecommunicationsMetric (unit)

Abstract

fetched live from OpenAlex

As a widely used over-the-top (OTT) video technique, adaptive bitrate video streaming has attracted considerable research attention and efforts. Traditional bitrate adaptation schemes rely on quality of service (QoS) to assess video delivery quality. However, in this paper, we argue that QoS may not accurately reflect the user-perceived video quality, especially in time-varying wireless networks. Instead, we adopt the concept of quality of experience (QoE) and propose a novel caching-based adaptation scheme to improve the overall QoE, which can more comprehensively gauge the subjective user satisfaction of video quality. Experiments show that the proposed method outperforms existing adaptation schemes in terms of QoE in various network scenarios. This observation is mainly attributed to the merits of our scheme in properly leveraging channel bandwidth prediction and proxy-based content prefetching.

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 categoriesMeta-epidemiology (narrow)
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 score0.967
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.090
GPT teacher head0.353
Teacher spread0.263 · 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