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Record W3159136916

Quality of Experience Assessment of Over-The-Top Video Streaming

2017· dissertation· en· W3159136916 on OpenAlexfundno aff
Weiwei Li

Bibliographic record

VenueTSpace (University of Toronto) · 2017
Typedissertation
Languageen
FieldComputer Science
TopicImage and Video Quality Assessment
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsQuality (philosophy)Quality assessmentComputer scienceMultimediaEngineeringOperations managementExternal quality assessmentEpistemology
DOInot available

Abstract

fetched live from OpenAlex

The new technology of mobile communication has provided nutritious soil for the growth of mobile services, but also invoked the user's expectation for higher quality service. To survive and prosper in the ever-changing service landscape, service providers seriously consider Quality of Experience (QoE). QoE concerns service quality from the user's perspective.\nThis dissertation reports our research to analyze and assess QoE of the Over-The-Top (OTT) video service. It has proposed to study QoE based on the life cycle of a video session. Based on the concept of a life cycle, it classifies interruptions during a video playback as impairments (temporary interruptions) and failures (permanent interruptions).\nThis dissertation introduces a detailed subjective methodology for assessing session-based QoE in a laboratory controllable approach. The methodology includes selection of video content, design and implement of test conditions, delivery of questionnaire, to data collection. It is eminently useful for service providers and researchers who are interested in assessing video QoE. Three laboratory controllable experiments have been conducted following this methodology.\nThis dissertation also provides QoE objective assessment based on data collected from these subjective experiments. First, the dissertation presents the impact of failures on the QoE evaluation by statistical tests, which explains why we need to study session-based QoE. Second, it discusses the interplay among selecting QoE factors, CQ (Content Quality), TQ (Technical Quality), OX (Overall eXperiment), and acceptability. It demonstrates that TQ is the determinant factor of OX with the presence of failures in these laboratory controllable experiments, which is valuable for future session-based QoE studies. And then, the dissertation investigates the relationship between QoE factors and Application Performance Metrics (APMs). Proposing novel APMs to predict QoE factors, the feasibility of APMs is examined and a primary QoE model is proposed. Lastly, this dissertation identifies that the user's rating behaviors have different levels of sensitivity to impairments and failures. Principle Component Analysis (PCA) is proposed to discern abnormal behaviors and classify user personality in QoE assessment.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.036
GPT teacher head0.371
Teacher spread0.336 · 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 teacher head, not a consensus.

Study designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

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