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Coal not diamonds: how memory pressure falters mobile video QoE

Authors:

Ihsan Ayyub Qazi,

Zafar Ayyub QaziAuthors Info & Claims

CoNEXT '22: Proceedings of the 18th International Conference on emerging Networking EXperiments and Technologies

Pages 307 - 320

https://doi.org/10.1145/3555050.3569120

Published: 30 November 2022 Publication History

Abstract

The popularity of video streaming on smartphones has led to rising demands for high-quality mobile video streaming. Consequently, we are observing growing support for higher resolution videos (e.g., HD, FHD, QHD) and higher video frame rates (e.g., 48 FPS, 60 FPS). However, supporting high-quality video streaming on smartphones introduces new challenges---besides the available network capacity, the smartphone itself can become a bottleneck due to resource constraints, such as low available memory. In this paper, we conduct an in-depth investigation of memory usage on smartphones and its impacts on mobile video streaming. Our investigation - driven by a combination of a user study, user survey, and experiments on real smartphones - reveals that (i) most smartphones observe memory pressure (i.e., low available memory scenarios), (ii) memory pressure can have a significant impact on mobile video QoE when streaming high-quality videos, e.g., resulting in the mean frame drop rate of 9--100% across smartphones and significantly lower user ratings, and (iii) the drop in mobile video QoE happens primarily due to the way in which video processes interact with kernel-level memory management mechanism, with opportunities for improving mobile video QoE through better adaptation by video clients.

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Cited By

Kim TKim B(2024)Techniques for Detecting the Start and End Points of Sign Language Utterances to Enhance Recognition Performance in Mobile EnvironmentsApplied Sciences10.3390/app1420919914:20(9199)Online publication date: 10-Oct-2024
https://doi.org/10.3390/app14209199
Chen TLin YChristianson NAkhtar ZDharmaji SHajiesmaili MWierman ASitaraman RSekar VYu MSeneviratne AVeitch D(2024)SODA: An Adaptive Bitrate Controller for Consistent High-Quality Video StreamingProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672260(613-644)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3651890.3672260
Munir MSaeed MAli MQazi ZRaza AQazi I(2023)Learning Fast and Slow: Towards Inclusive Federated LearningMachine Learning and Knowledge Discovery in Databases: Research Track10.1007/978-3-031-43415-0_23(384-401)Online publication date: 18-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-43415-0_23

Index Terms

Coal not diamonds: how memory pressure falters mobile video QoE
1. General and reference
  1. Cross-computing tools and techniques
    1. Experimentation
    2. Measurement
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile devices

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cover image ACM Conferences

CoNEXT '22: Proceedings of the 18th International Conference on emerging Networking EXperiments and Technologies

November 2022

431 pages

ISBN:9781450395083

DOI:10.1145/3555050

General Chairs:
Giuseppe Bianchi
University of Rome Tor Vergata, Italy
,
Alessandro Mei
Sapienza University of Rome, Italy

Copyright © 2022 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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New York, NY, United States

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Published: 30 November 2022

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CoNEXT '22: The 18th International Conference on emerging Networking EXperiments and Technologies

December 6 - 9, 2022

Roma, Italy

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CoNEXT '22 Paper Acceptance Rate 28 of 151 submissions, 19%;

Overall Acceptance Rate 198 of 789 submissions, 25%

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Cited By

Kim TKim B(2024)Techniques for Detecting the Start and End Points of Sign Language Utterances to Enhance Recognition Performance in Mobile EnvironmentsApplied Sciences10.3390/app1420919914:20(9199)Online publication date: 10-Oct-2024
https://doi.org/10.3390/app14209199
Chen TLin YChristianson NAkhtar ZDharmaji SHajiesmaili MWierman ASitaraman RSekar VYu MSeneviratne AVeitch D(2024)SODA: An Adaptive Bitrate Controller for Consistent High-Quality Video StreamingProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672260(613-644)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3651890.3672260
Munir MSaeed MAli MQazi ZRaza AQazi I(2023)Learning Fast and Slow: Towards Inclusive Federated LearningMachine Learning and Knowledge Discovery in Databases: Research Track10.1007/978-3-031-43415-0_23(384-401)Online publication date: 18-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-43415-0_23

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