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Record W3213030705 · doi:10.32920/ryerson.14644218.v1

A Hybrid Rate Adaptation Framework for MPEG-4 FGS Video Streaming Over IP

2021· preprint· en· W3213030705 on OpenAlexaff
Colin Xialin Huang

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceComputer networkMulticastQuality of serviceThe InternetNetwork packetPacket lossCommunication sourceReal-time computingRTP Control ProtocolTransmission (telecommunications)Bandwidth (computing)MPEG-4Coding (social sciences)Telecommunications

Abstract

fetched live from OpenAlex

There are increasing demands for real-time streaming video applications over the Internet. However, the current generation Internet was not originally designed for real-time streaming applications and only provides best-effort services, so there are many challenges in the deployment of video streaming applications over the Internet. This thesis investigates a hybrid end-to-end rate adaptation framework that provides application-level enhancements to achieve Quality of Service (QoS) for MPEG-4 FGS-Encoded video bandwidth on the path and the terminal process capabilities based on the packet-loss ratio and then determine their subscribing rate of video streams. The sender adjusts the transmission rate based on the packet-loss ratio and then determine their subscribing rate of video streams. The sender adjusts the transmission rate based on the proportion of load status feedbacks from the receivers. The sender and the receivers act together to minimize the possibility of network congestion by adjusting the transmission rate to match the network conditions. This framework achieves inter-receiver fairness in a heterogeneous multicast environment and improves QoS stability for MPEG-4 FGS video streaming over the Internet.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.682
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.003
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 head0.044
GPT teacher head0.291
Teacher spread0.247 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

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