Joint Resource Optimization for Adaptive Multimedia Services in MEC-Based Vehicular Networks

DOI number:10.1109/GLOBECOM38437.2019.9013434

Journal:2019 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)

Place of Publication:Waikoloa, HI

Abstract:Mobile edge computing (MEC) has been an emerging paradigm to support low-latency applications in vehicular networks by offloading resources at network edge. However, it is still challenging to apply MEC-based architecture to implement multimedia services due to varying wireless communication, high vehicle mobility and heterogeneous resource integration. In this paper, we investigate adaptive-bitrate (ABR)-based multimedia services (MS) in MEC-based vehicular networks, where each multimedia file is divided into multiple chunks and can be requested at different bitrate levels. Further, MEC servers can satisfy local vehicular requests by integrating hetero-geneous cache and communication resources. Based on the above observation, we formulate joint resource optimization (JSO) problem by synthesizing cache placement, wireless bandwidth allocation and chunk quality adaptation. On this basis, we propose a reinforcement-learning-based cache placement (RLCP) algorithm, which determines the optimal offloaded chunks by learning the global knowledge of cache reward in an iterative way. Further, we design an adaptive-quality-based chunk selection (AQCS) algorithm, which can be adaptive to timevarying wireless channel by dynamically adjusting bandwidth allocation and quality level based on real-time service workload. Lastly, we build the simulation model and conduct an extensive performance evaluation, which demonstrates the superiority of proposed algorithms.

Co-author:Penglin Dai,Kai Liu,Xiao Wu,Huanlai Xing,Jing Xu,Victor CS Lee

Document Code:10.1109/GLOBECOM38437.2019.9013434

ISSN No.:2334-0983

Translation or Not:no

Date of Publication:2019-12-13