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A scheduling framework for adaptive video delivery over cellular networks

Authors:

Rajesh Mahindra,

Mohammad Amir Khojastepour,

Sampath Rangarajan,

Mung ChiangAuthors Info & Claims

MobiCom '13: Proceedings of the 19th annual international conference on Mobile computing & networking

Pages 389 - 400

https://doi.org/10.1145/2500423.2500433

Published: 30 September 2013 Publication History

Abstract

As the growth of mobile video traffic outpaces that of cellular network speed, industry is adopting HTTP-based adaptive video streaming technology which enables dynamic adaptation of video bit-rates to match changing network conditions. However, recent measurement studies have observed problems in fairness, stability, and efficiency of resource utilization when multiple adaptive video flows compete for bandwidth on a shared wired link. Through experiments and simulations, we confirm that such undesirable behavior manifests itself in cellular networks as well. To overcome these problems, we design an in-network resource management framework, AVIS, that schedules HTTP-based adaptive video flows on cellular networks. AVIS effectively manages the resources of a cellular base station across adaptive video flows. AVIS also provides a framework for mobile operators to achieve a desired balance between optimal resource allocation and user quality of experience. AVIS has three key differentiating features: (1) It optimally computes the bit-rate allocation for each user, (2) It includes a scheduler and per-flow shapers to enforce bit-rate stability of each flow and (3) It leverages the resource virtualization technique to separate resource management of adaptive video flows from regular video flows. We implement a prototype system of AVIS and evaluate it on both a WiMAX network testbed and a LTE system simulator to show its efficacy and scalability.

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Cited By

Kalan RDulger I(2024)A Survey on QoE Management Schemes for HTTP Adaptive Video Streaming: Challenges, Solutions, and OpportunitiesIEEE Access10.1109/ACCESS.2024.349161312(170803-170839)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3491613
Kang JChung K(2023)Adaptive Streaming Scheme with Reinforcement Learning in Edge Computing Environments2023 International Conference on Information Networking (ICOIN)10.1109/ICOIN56518.2023.10048966(128-133)Online publication date: 11-Jan-2023
https://doi.org/10.1109/ICOIN56518.2023.10048966
Kim MChung K(2023)HTTP adaptive streaming scheme based on reinforcement learning with edge computing assistanceJournal of Network and Computer Applications10.1016/j.jnca.2023.103604213(103604)Online publication date: Apr-2023
https://doi.org/10.1016/j.jnca.2023.103604
Show More Cited By

Index Terms

A scheduling framework for adaptive video delivery over cellular networks
1. Hardware
  1. Communication hardware, interfaces and storage
2. Networks
  1. Network types
    1. Mobile networks
    2. Wireless access networks

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Published In

cover image ACM Conferences

MobiCom '13: Proceedings of the 19th annual international conference on Mobile computing & networking

September 2013

504 pages

ISBN:9781450319997

DOI:10.1145/2500423

General Chair:
Sumi Helal
University of Florida, USA
,
Program Chairs:
Ranveer Chandra
Microsoft, USA
,
Robin Kravets
University of Illinois, Urbana-Champaign, USA

Copyright © 2013 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]

Sponsors

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 September 2013

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MobiCom'13

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SIGMOBILE

MobiCom'13: The 19th Annual International Conference on Mobile Computing and Networking

September 30 - October 4, 2013

Florida, Miami, USA

Acceptance Rates

MobiCom '13 Paper Acceptance Rate 28 of 207 submissions, 14%;

Overall Acceptance Rate 440 of 2,972 submissions, 15%

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Cited By

Kalan RDulger I(2024)A Survey on QoE Management Schemes for HTTP Adaptive Video Streaming: Challenges, Solutions, and OpportunitiesIEEE Access10.1109/ACCESS.2024.349161312(170803-170839)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3491613
Kang JChung K(2023)Adaptive Streaming Scheme with Reinforcement Learning in Edge Computing Environments2023 International Conference on Information Networking (ICOIN)10.1109/ICOIN56518.2023.10048966(128-133)Online publication date: 11-Jan-2023
https://doi.org/10.1109/ICOIN56518.2023.10048966
Kim MChung K(2023)HTTP adaptive streaming scheme based on reinforcement learning with edge computing assistanceJournal of Network and Computer Applications10.1016/j.jnca.2023.103604213(103604)Online publication date: Apr-2023
https://doi.org/10.1016/j.jnca.2023.103604
Rahman WHuh E(2023)Content-aware QoE optimization in MEC-assisted Mobile video streamingMultimedia Tools and Applications10.1007/s11042-023-15163-w82:27(42053-42085)Online publication date: 4-Apr-2023
https://doi.org/10.1007/s11042-023-15163-w
Xie YJamieson K(2022)NG-ScopeProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/35080326:1(1-26)Online publication date: 28-Feb-2022
https://dl.acm.org/doi/10.1145/3508032
Kim MChung K(2022)Reinforcement Learning-Based HTTP Adaptive Streaming Scheme in Edge Computing EnvironmentsProcedia Computer Science10.1016/j.procs.2022.03.004201(8-15)Online publication date: 2022
https://doi.org/10.1016/j.procs.2022.03.004
SAKAMOTO RSHOBUDANI THOTCHI RKUBO R(2021)QoE-Aware Stable Adaptive Video Streaming Using Proportional-Derivative Controller for MPEG-DASHIEICE Transactions on Communications10.1587/transcom.2020EBP3038E104.B:3(286-294)Online publication date: 1-Mar-2021
https://doi.org/10.1587/transcom.2020EBP3038
Jiang YSun BTsang D(2021) Not Taken for Granted: Configuring Scalable Live Video Streaming Under Throughput Fluctuations in Mobile Edge Networks IEEE Transactions on Vehicular Technology10.1109/TVT.2021.305867670:3(2771-2782)Online publication date: Mar-2021
https://doi.org/10.1109/TVT.2021.3058676
Kim MChung K(2021)Edge Computing Assisted Adaptive Streaming Scheme for Mobile NetworksIEEE Access10.1109/ACCESS.2020.30473739(2142-2152)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2020.3047373
Narayanan ARamadan EMehta RHu XLiu QFezeu RDayalan UVerma SJi PLi TQian FZhang Z(2020)Lumos5GProceedings of the ACM Internet Measurement Conference10.1145/3419394.3423629(176-193)Online publication date: 27-Oct-2020
https://dl.acm.org/doi/10.1145/3419394.3423629
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