Optimal media service selection scheme for mobile users in mobile cloud

  • Li Chunlin
  • Meng Chuanli
  • Chen Yi
  • Luo Youlong


Media cloud environments can provide a large number of multimedia services to mobile clients due to its flexibility and agility. However, a number of challenges need to be addressed so that these services are efficiently provided in terms of resources’ usage and energy consumption whilst improving the quality of service (QoS) and the user’s service satisfaction. This paper proposes a new media cloud distributed scheduling scheme that addresses these challenges, suitable for resource-intensive mobile application such as mobile video streaming. The proposed scheduling policy includes media service provisioning and cloud resource scheduling within the cloud datacenter, being able to jointly improve the mobile user’s satisfaction and the media cloud supplier’s revenue. Its aims are to minimize the service time, power consumption and costs for the service provider, through a convenient tradeoff of multiple QoS parameters and, consequently, increase the user’s satisfaction by reducing waiting times, service failure rate and power consumption of the mobile device. The validity of the proposed scheme is demonstrated by running experiments based on a practical use case of video streaming for mobile clients. The experiments were defined to allow to study the effects of request rate, video length, number of video streams and job size on the performance of the proposed media cloud distributed scheduling algorithm and compare it with related algorithms. The results show that proposed algorithm has better performance in terms of request failure rate, amount of energy consumed and response time.


Media cloud Media service provisioning Resource-intensive task Mobile application 



The work was supported by the National Natural Science Foundation (NSF) under Grants (Nos. 61472294, 61672397, 61771354), the Fundamental Research Funds for the Central Universities (No. 2017-YS-063), Open Research Fund of Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University (No. BKBD-2017KF01). Any opinions, findings, and conclusions are those of the authors and do not necessarily reflect the views of the above agencies.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Li Chunlin
    • 1
    • 2
    • 3
  • Meng Chuanli
    • 1
  • Chen Yi
    • 2
  • Luo Youlong
    • 1
  1. 1.Department of Computer ScienceWuhan University of TechnologyWuhanPeople’s Republic of China
  2. 2.Beijing Key Laboratory of Big Data Technology for Food SafetyBeijing Technology and Business UniversityBeijingPeople’s Republic of China
  3. 3.Provincial Key Laboratory for Computer Information Processing TechnologySoochow UniversitySuzhouPeople’s Republic of China

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