research-article

A Measurement Study on Achieving Imperceptible Latency in Mobile Cloud Gaming

Authors:

Teemu Kämäräinen,

Matti Siekkinen,

Antti Ylä-Jääski,

Pan HuiAuthors Info & Claims

MMSys'17: Proceedings of the 8th ACM on Multimedia Systems Conference

Pages 88 - 99

https://doi.org/10.1145/3083187.3083191

Published: 20 June 2017 Publication History

Abstract

Cloud gaming is a relatively new paradigm in which the game is rendered in the cloud and is streamed to an end-user device through a thin client. Latency is a key challenge for cloud gaming. In order to optimize the end-to-end latency, it is first necessary to understand how the end-to-end latency builds up from the mobile device to the cloud gaming server. In this paper we dissect the delays occurring in the mobile device and measure access delays in various networks and network conditions. We also perform a Europe-wide latency measurement study to find the optimal server locations and see how the number of server locations affects the network delay. The results are compared to limits found for perceivable delays in recent human-computer interaction studies. We show that the limits can be achieved only with the latest mobile devices with specific control methods. In addition, we study the expected latency reduction by near future technological development and show that its potential impact is bigger on the end-to-end latency than that of replication of the service and server placement optimization.

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A Measurement Study on Achieving Imperceptible Latency in Mobile Cloud Gaming

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cover image ACM Conferences

MMSys'17: Proceedings of the 8th ACM on Multimedia Systems Conference

June 2017

407 pages

ISBN:9781450350020

DOI:10.1145/3083187

Copyright © 2017 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: 20 June 2017

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Research-article
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Research Grants Council of Hong Kong
Suomen Akatemia
Nokia Center for Advanced Research
Tekes
Innovation and Technology Commission - Hong Kong

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MMSys'17

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SIGMM

MMSys'17: Multimedia Systems Conference 2017

June 20 - 23, 2017

Taipei, Taiwan

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MMSys'17 Paper Acceptance Rate 13 of 47 submissions, 28%;

Overall Acceptance Rate 176 of 530 submissions, 33%

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Cited By

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https://doi.org/10.3390/app14041397
Lyu MWang YSivaraman V(2024)Do Cloud Games Adapt to Client Settings and Network Conditions?2024 IFIP Networking Conference (IFIP Networking)10.23919/IFIPNetworking62109.2024.10619817(482-488)Online publication date: 3-Jun-2024
https://doi.org/10.23919/IFIPNetworking62109.2024.10619817
Lyu MMadanapalli SVishwanath ASivaraman V(2024)Network Anatomy and Real-Time Measurement of Nvidia GeForce NOW Cloud GamingPassive and Active Measurement10.1007/978-3-031-56249-5_3(61-91)Online publication date: 20-Mar-2024
https://doi.org/10.1007/978-3-031-56249-5_3
Pan DLi ZLing LFei G(2023)FLOW: Filtering and LSTM-based Optimization for Web Browser InteractionsProceedings of the Eleventh International Symposium of Chinese CHI10.1145/3629606.3629611(37-43)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3629606.3629611
Marchal XGraff PKy JCholez TTuffin SMathieu BFestor O(2023)An Analysis of Cloud Gaming Platforms Behaviour Under Synthetic Network Constraints and Real Cellular Networks ConditionsJournal of Network and Systems Management10.1007/s10922-023-09720-931:2Online publication date: 17-Feb-2023
https://doi.org/10.1007/s10922-023-09720-9
Ky JMathieu BLahmadi ABoutaba R(2022)Assessing Unsupervised Machine Learning solutions for Anomaly Detection in Cloud Gaming Sessions2022 18th International Conference on Network and Service Management (CNSM)10.23919/CNSM55787.2022.9964533(367-373)Online publication date: 31-Oct-2022
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Gao YZhang CXie ZQi ZZhou J(2022)Cost-Efficient and Quality-of-Experience-Aware Player Request Scheduling and Rendering Server Allocation for Edge-Computing-Assisted Multiplayer Cloud GamingIEEE Internet of Things Journal10.1109/JIOT.2021.31328499:14(12029-12040)Online publication date: 15-Jul-2022
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Metzger FGeißler SGrigorjew ALoh FMoldovan CSeufert MHoßfeld T(2022)An Introduction to Online Video Game QoS and QoE Influencing FactorsIEEE Communications Surveys & Tutorials10.1109/COMST.2022.317725124:3(1894-1925)Online publication date: 1-Jul-2022
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Peltonen EAhmad IAral ACapobianco MDing AGil-Castineira FGilman EHarjula EJurmu MKarvonen TKelanti MLeppanen TLoven LMikkonen TMohan NNurmi PPirttikangas SSroka PTarkoma SYang T(2022)The Many Faces of Edge IntelligenceIEEE Access10.1109/ACCESS.2022.321058410(104769-104782)Online publication date: 2022
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