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A Receiver-Driven Loss Recovery Mechanism for Video Dissemination over Information-Centric VANET
( Longzhe Han ),( Xuecai Bao ),( Wenfeng Wang ),( Xiangsheng Feng ),( Zuhan Liu ),( Wenqun Tan ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.7
Information-Centric Vehicular Ad Hoc Network (IC-VANET) is a promising network architecture for the future intelligent transport system. Video streaming applications over IC-VANET not only enrich infotainment services, but also provide the drivers and pedestrians real-time visual information to make proper decisions. However, due to the characteristics of wireless link and frequent change of the network topology, the packet loss seriously affects the quality of video streaming applications. In this paper, we propose a REceiver-Driven loss reCOvery Mechanism (REDCOM) to enhance video dissemination over IC-VANET. A Markov chain based estimation model is introduced to capture the real-time network condition. Based on the estimation result, the proposed REDCOM recovers the lost packets by requesting additional forward error correction packets. The REDCOM follows the receiver-driven model of IC-VANET and does not require the infrastructure support to efficiently overcome packet losses. Experimental results demonstrate that the proposed REDCOM improves video quality under various network conditions.
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( Longzhe Han ),( Taras Maksymyuk ),( Xuecai Bao ),( Jia Zhao ),( Yan Liu ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.9
Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches.
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