research-article

Candid with YouTube: Adaptive Streaming Behavior and Implications on Data Consumption

Authors:

Chander Govindarajan,

Pradipta De,

Niloy Ganguly,

Sandip ChakrabortyAuthors Info & Claims

NOSSDAV'17: Proceedings of the 27th Workshop on Network and Operating Systems Support for Digital Audio and Video

Pages 19 - 24

https://doi.org/10.1145/3083165.3083177

Published: 20 June 2017 Publication History

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Abstract

YouTube has emerged as the largest player among video streaming services, serving video content for users using DASH. Research studies on various aspects of YouTube, especially its streaming service, abound in the literature. However, these works study YouTube streaming from the periphery, and report results based on their understanding of general DASH recommendations. In this study, we explore in depth YouTube's implementation of the DASH client. We identify important parameters in YouTube's rate adaptation algorithm, and study their roles. In a departure from existing literature, we observe that YouTube opportunistically adapts segment length, in addition to quality level, in response to bandwidth fluctuations. We report that this scheme results in a much lower average data wastage ratio (0.82x10-6), than reported earlier. We also propose an analytical model, augmented with a machine learning based classifier (with average accuracy of 85.75%), to predict data consumption for a playback session in advance.

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Wang YLyu MSivaraman VVallina-Rodríguez NSuarez-Tángil GLevin DPelsser C(2024)Characterizing User Platforms for Video Streaming in Broadband NetworksProceedings of the 2024 ACM on Internet Measurement Conference10.1145/3646547.3688435(563-579)Online publication date: 4-Nov-2024
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Chaudhary SShukla NSachdeva PChakraborty SMaity M(2024)Managing Connections by QUIC-TCP Racing: A First Look of Streaming Media Performance Over Popular HTTP/3 BrowsersIEEE Transactions on Network and Service Management10.1109/TNSM.2024.337106921:3(2962-2976)Online publication date: Jun-2024
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NOSSDAV'17: Proceedings of the 27th Workshop on Network and Operating Systems Support for Digital Audio and Video

June 2017

105 pages

ISBN:9781450350037

DOI:10.1145/3083165

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 20 June 2017

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MMSys'17: Multimedia Systems Conference 2017

June 20 - 23, 2017

Taipei, Taiwan

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NOSSDAV'17 Paper Acceptance Rate 15 of 40 submissions, 38%;

Overall Acceptance Rate 118 of 363 submissions, 33%

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Wang YLyu MSivaraman VVallina-Rodríguez NSuarez-Tángil GLevin DPelsser C(2024)Characterizing User Platforms for Video Streaming in Broadband NetworksProceedings of the 2024 ACM on Internet Measurement Conference10.1145/3646547.3688435(563-579)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3646547.3688435
Chaudhary SShukla NSachdeva PChakraborty SMaity M(2024)Managing Connections by QUIC-TCP Racing: A First Look of Streaming Media Performance Over Popular HTTP/3 BrowsersIEEE Transactions on Network and Service Management10.1109/TNSM.2024.337106921:3(2962-2976)Online publication date: Jun-2024
https://doi.org/10.1109/TNSM.2024.3371069
Liu JLerner DChung JPaul UGupta ABelding E(2024)Watching Stars in Pixels: The Interplay Of Traffic Shaping and YouTube Streaming QoE over GEO Satellite NetworksPassive and Active Measurement10.1007/978-3-031-56252-5_8(153-169)Online publication date: 20-Mar-2024
https://doi.org/10.1007/978-3-031-56252-5_8
Huang TZhou CZhang RWu CSun L(2023)Buffer Awareness Neural Adaptive Video Streaming for Avoiding Extra Buffer ConsumptionIEEE INFOCOM 2023 - IEEE Conference on Computer Communications10.1109/INFOCOM53939.2023.10229002(1-10)Online publication date: 17-May-2023
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