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Streaming content from a vehicular cloud

Published: 03 October 2016 Publication History

Abstract

Network densification via small cells is considered as a key step to cope with the data tsunami. Caching data at small cells or even user devices is also considered as a promising way to alleviate the backhaul congestion this densification might cause. However, the former suffers from high deployment and maintenance costs, and the latter from limited resources and privacy issues with user devices. We argue that an architecture with (public or private) vehicles acting as mobile caches and communication relays might be a promising middle ground. In this paper, we assume such a vehicular cloud is in place to provide video streaming to users, and that the operator can decide which content to store in the vehicle caches. Users can then greedily fill their playout buffer with video pieces of the streamed content from encountered vehicles, and turn to the infrastructure immediately when the playout buffer is empty, to ensure uninterrupted streaming. Our main contribution is to model the playout buffer in the user device with a queuing approach, and to provide a mathematical formulation for the idle periods of this buffer, which relate to the bytes downloaded from the cellular infrastructure. We also solve the resulting content allocation problem, and perform trace-based simulations to finally show that up to 50% of the original traffic could be offloaded from the main infrastructure.

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Cited By

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  • (2024)Multi-Path Transmission Protocol for Video Streaming Over Vehicular Fog Computing EnvironmentsIEEE Access10.1109/ACCESS.2024.341728812(87199-87216)Online publication date: 2024
  • (2022)Resource Allocation of Video Streaming Over Vehicular Networks: A Survey, Some Research Issues and ChallengesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.306520923:7(5955-5975)Online publication date: Jul-2022
  • (2022)Balancing QoS and Security in the Edge: Existing Practices, Challenges, and 6G Opportunities With Machine LearningIEEE Communications Surveys & Tutorials10.1109/COMST.2022.319169724:4(2419-2448)Online publication date: Dec-2023
  • Show More Cited By

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    cover image ACM Other conferences
    CHANTS '16: Proceedings of the Eleventh ACM Workshop on Challenged Networks
    October 2016
    92 pages
    ISBN:9781450342568
    DOI:10.1145/2979683
    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

    Publication History

    Published: 03 October 2016

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    Author Tags

    1. caching
    2. mobile data offloading
    3. multimedia streaming
    4. opportunistic networks
    5. optimization
    6. vehicular networks

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    MobiCom'16

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    CHANTS '16 Paper Acceptance Rate 14 of 27 submissions, 52%;
    Overall Acceptance Rate 61 of 159 submissions, 38%

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    Cited By

    View all
    • (2024)Multi-Path Transmission Protocol for Video Streaming Over Vehicular Fog Computing EnvironmentsIEEE Access10.1109/ACCESS.2024.341728812(87199-87216)Online publication date: 2024
    • (2022)Resource Allocation of Video Streaming Over Vehicular Networks: A Survey, Some Research Issues and ChallengesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.306520923:7(5955-5975)Online publication date: Jul-2022
    • (2022)Balancing QoS and Security in the Edge: Existing Practices, Challenges, and 6G Opportunities With Machine LearningIEEE Communications Surveys & Tutorials10.1109/COMST.2022.319169724:4(2419-2448)Online publication date: Dec-2023
    • (2020)A Survey of Vehicular Cloud Research: Trends, Applications and ChallengesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.295974321:6(2648-2663)Online publication date: Jun-2020
    • (2019)Toward Approximating Job Completion Time in Vehicular CloudsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2018.287399820:8(3168-3177)Online publication date: Aug-2019
    • (2018)Increasing Network Resiliency via Data-Centric Offloading2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)10.1109/WiMOB.2018.8589179(270-277)Online publication date: Oct-2018
    • (2018)A Two-Step Chunk-Based Algorithm for Offloading Streaming Traffic Through a Vehicular Cloud2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)10.1109/SPAWC.2018.8445782(1-5)Online publication date: Jun-2018
    • (2018)Understanding Complementary Multi-layer Collaborative Heuristics for Adaptive Caching in Heterogeneous Mobile Opportunistic Networks2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC)10.1109/IWCMC.2018.8450536(880-885)Online publication date: Jun-2018
    • (2018)Adaptive Real-Time Predictive Collaborative Content Discovery and Retrieval in Mobile Disconnection Prone NetworksIEEE Access10.1109/ACCESS.2018.28400406(32188-32206)Online publication date: 2018

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