An adaptive loss protection for video transmission over content-centric networks

Han, Longzhe; Kang, Seung-Seok; In, Hoh Peter

doi:10.1007/s11042-014-1874-9

Cited by 1 publication

(4 citation statements)

References 11 publications

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“…To evaluate the efficiency of our DL-LRM mechanism, comparing to the Adaptive Loss Protection scheme (ALP-CCN) proposed in [19] we have conducted simulations in the Network Simulator 3. The experimental network topology follows the structure shown in Fig.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Bai et al applied the information centric architecture to the vehicular ad-hoc networks and proposed various new types of network applications [32]. For the problem of video packet loss in wireless ICN, Han et al proposed a adaptive FEC algorithm [19]. However, the proposed algorithm in [19] used video server to produce FEC packets and did not exploit the advantages of the MEC.…”

Section: Related Workmentioning

confidence: 99%

“…For the problem of video packet loss in wireless ICN, Han et al proposed a adaptive FEC algorithm [19]. However, the proposed algorithm in [19] used video server to produce FEC packets and did not exploit the advantages of the MEC.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the architectural convergence of MEC and ICN, in this paper we propose a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming to overcome wireless packet loss. The DL-LRM consists of deep Q-learning based FEC control algorithm and content caching detection based retransmission algorithm [19][20][21]. In difference to existing approaches, we utilize the computing functionality provided by the MEC to produce FEC packets.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning based Loss Recovery Mechanism for Video Streaming over Mobile Information-Centric Network

2019

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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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Section: Experimental Results and Analysismentioning

confidence: 99%