Deakin University
Browse

File(s) under permanent embargo

Optimising DASH over AQM-enabled gateways using intra-chunk parallel retrieval (chunklets)

conference contribution
posted on 2017-01-01, 00:00 authored by Jonathan KuaJonathan Kua, G Armitage
© 2017 IEEE. Multimedia streaming is a significant source of Internet traffic, with Netflix and YouTube accounting for more than 50% of North American fixed network peak download traffic in 2016. Dynamic Adaptive Streaming over HTTP (DASH) is a recent standard for live and on-demand video streaming services, where clients adapt the video quality on-the-fly to match the network capacity by requesting multi-rate video chunk-by-chunk. Emerging Active Queue Management (AQM) schemes such as PIE and FQ-CoDel are being progressively deployed either at the ISP-end and/or home gateway to counter bufferbloat and will impact consumer DASH streams. We propose using intra-chunk parallel connections (chunklets) to retrieve DASH content when bottlenecks implement AQMs. We experimentally evaluate and characterise the impact of using chunklets over traditional FIFO, symmetric/asymmetric PIE and FQ-CoDel AQM bottlenecks. We show FQ-CoDel's flow isolation and fair capacity sharing ability enables DASH chunklets to attain the best throughput multiplication effect, hence translating to better user experience in the presence of competing elastic flows.

History

Event

Computer Communication and Networks. Conference (26th : 2017 : Vancouver, British Columbia)

Pagination

1 - 9

Publisher

IEEE

Location

Vancouver, British Columbia

Place of publication

Piscataway, N.J.

Start date

2017-07-31

End date

2017-08-03

ISBN-13

9781509029914

Language

eng

Publication classification

E1.1 Full written paper - refereed

Title of proceedings

ICCCN 2017 : Proceedings of the 26th International Conference on Computer Communication and Networks

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC