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YouTube everywhere: impact of device and infrastructure synergies on user experience

Authors:

Alessandro Finamore,

Maurizio M. Munafò,

Sanjay G. RaoAuthors Info & Claims

IMC '11: Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference

Pages 345 - 360

https://doi.org/10.1145/2068816.2068849

Published: 02 November 2011 Publication History

Abstract

In this paper we present a complete measurement study that compares YouTube traffic generated by mobile devices (smart-phones,tablets) with traffic generated by common PCs (desktops, notebooks, netbooks). We investigate the users' behavior and correlate it with the system performance. Our measurements are performed using unique data sets which are collected from vantage points in nation-wide ISPs and University campuses from two countries in Europe and the U.S.

Our results show that the user access patterns are similar across a wide range of user locations, access technologies and user devices. Users stick with default player configurations, e.g., not changing video resolution or rarely enabling full screen playback. Furthermore it is very common that users abort video playback, with 60% of videos watched for no more than 20% of their duration.

We show that the YouTube system is highly optimized for PC access and leverages aggressive buffering policies to guarantee excellent video playback. This however causes 25%-39% of data to be unnecessarily transferred, since users abort the playback very early. This waste of data transferred is even higher when mobile devices are considered. The limited storage offered by those devices makes the video download more complicated and overall less efficient, so that clients typically download more data than the actual video size. Overall, this result calls for better system optimization for both, PC and mobile accesses.

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Cited By

Alok AHuq FUllah SGhosh RNoor J(2024)Navigating Truth in the Sea of Content: Exploring Influential Factors Shaping User Perceptions of Trustworthiness in YouTube ContentProceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies10.1145/3674829.3675065(107-116)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3674829.3675065
Su HWang SYang SHuang TRen X(2024)Reducing Traffic Wastage in Video Streaming via Bandwidth-Efficient Bitrate AdaptationIEEE Transactions on Mobile Computing10.1109/TMC.2024.337349823:11(10361-10377)Online publication date: Nov-2024
https://doi.org/10.1109/TMC.2024.3373498
Lee AKo TKim C(2024)Design of User Behavior-aware Video Chunk Caching Strategy at Network Edge2024 Fifteenth International Conference on Ubiquitous and Future Networks (ICUFN)10.1109/ICUFN61752.2024.10625477(513-515)Online publication date: 2-Jul-2024
https://doi.org/10.1109/ICUFN61752.2024.10625477
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Index Terms

YouTube everywhere: impact of device and infrastructure synergies on user experience

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Published In

cover image ACM Conferences

IMC '11: Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference

November 2011

612 pages

ISBN:9781450310130

DOI:10.1145/2068816

Program Chairs:
Patrick Thiran
EPFL, Switzerland
,
Walter Willinger
AT&T Labs-Research, USA

Copyright © 2011 ACM.

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: 02 November 2011

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IMC '11

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IMC '11: Internet Measurement Conference

November 2 - 4, 2011

Berlin, Germany

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Cited By

Alok AHuq FUllah SGhosh RNoor J(2024)Navigating Truth in the Sea of Content: Exploring Influential Factors Shaping User Perceptions of Trustworthiness in YouTube ContentProceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies10.1145/3674829.3675065(107-116)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3674829.3675065
Su HWang SYang SHuang TRen X(2024)Reducing Traffic Wastage in Video Streaming via Bandwidth-Efficient Bitrate AdaptationIEEE Transactions on Mobile Computing10.1109/TMC.2024.337349823:11(10361-10377)Online publication date: Nov-2024
https://doi.org/10.1109/TMC.2024.3373498
Lee AKo TKim C(2024)Design of User Behavior-aware Video Chunk Caching Strategy at Network Edge2024 Fifteenth International Conference on Ubiquitous and Future Networks (ICUFN)10.1109/ICUFN61752.2024.10625477(513-515)Online publication date: 2-Jul-2024
https://doi.org/10.1109/ICUFN61752.2024.10625477
Zhang YLiu YGuo LLee J(2023)Measurement of a Large-Scale Short-Video Service Over Mobile and Wireless NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2021.313989322:6(3472-3488)Online publication date: 1-Jun-2023
https://doi.org/10.1109/TMC.2021.3139893
Zhang GLiu KHu HAggarwal VLee J(2023)Post-Streaming Wastage Analysis – A Data Wastage Aware Framework in Mobile Video StreamingIEEE Transactions on Mobile Computing10.1109/TMC.2021.306976422:1(389-401)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TMC.2021.3069764
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
https://doi.org/10.1109/INFOCOM53939.2023.10229002
Dong BLiu TLi BZhou XWang SXu Z(2023)WebInf: Accelerating WebGPU-based In-browser DNN Inference via Adaptive Model Partitioning2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS60453.2023.00333(2499-2506)Online publication date: 17-Dec-2023
https://doi.org/10.1109/ICPADS60453.2023.00333
Tanjung DKim JKim DLee JKim SJung J(2023)QoE Optimization in DASH-Based Multiview Video StreamingIEEE Access10.1109/ACCESS.2023.330038011(83603-83614)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3300380
Cheng ZZhou MSabran K(2023)Mobile app-based interventions to improve the well-being of people with dementia: a systematic literature reviewAssistive Technology10.1080/10400435.2023.2206439(1-11)Online publication date: 11-May-2023
https://doi.org/10.1080/10400435.2023.2206439
Bartolec IOrsolic ISkorin-Kapov L(2022)Impact of User Playback Interactions on In-Network Estimation of Video Streaming PerformanceIEEE Transactions on Network and Service Management10.1109/TNSM.2022.318011419:3(3547-3561)Online publication date: Sep-2022
https://doi.org/10.1109/TNSM.2022.3180114
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