Live MPEG-DASH video streaming cache management with cognitive mobile edge computing
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ORIGINAL RESEARCH
Live MPEG‑DASH video streaming cache management with cognitive mobile edge computing Hung‑Yen Weng1 · Ren‑Hung Hwang2 · Chin‑Feng Lai1 Received: 6 July 2020 / Accepted: 11 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Video streaming is expected to account for up to 82% of network traffic by 2021 according to the forecast of CISCO’s Visual Networking Index. Dynamic Adaptive Streaming over HTTP (DASH) is the de facto protocol for delivering video streaming services over the Internet. As the cellular network entering the 5G era, more and more video streaming will be live video streaming delivered to mobile phones. However, due to a large amount of video traffic, several techniques are required to improve the user Quality of Experience (QoE). In this work, we first propose a network architecture design for delivering live video streaming over the cellular core network with cognitive Mobile Edge Computing (MEC) servers. We then focused on the optimal cache management by considering several issues, include QoE, cache size, backhaul bandwidth, pre-cache mechanism, and user mobility. Finally, we show a prototype of the proposed MEC-assisted live video streaming system. Our simulation results show the performance improvement of the proposed cache management schemes in terms of QoE-based system utility. Our prototype shows the significant latency reduction in receiving video streams with MEC pre-cache mechanism. Keywords Live video streaming · Cognitive mobile edge computing · Forward error correction · Quality of experience · Convex optimization · Device-to-device
1 Introduction In recent years, due to the technological advancement of 5G cellular networks and the prevalence of smartphones, video streaming data have accounted for the largest portion of network traffic. According to Cisco’s Complete Visual Networking Index (VNI) Forecast, 82% of all IP traffic will be video traffic. As more and more users use mobile phones to watch video streaming services, especially the live streaming, it also causes a huge bandwidth burden on the mobile network, both in backhaul (5G core) and radio access networks. Cognitive Mobile Edge Computing (MEC) is one of the key emerging technologies to effectively offload the bandwidth requirement (Milan et al. 2014; ETSI GS MEC * Ren‑Hung Hwang [email protected] 1
Department of Engineering Science, National Cheng Kung University, Tainan City, Taiwan
Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi County, Taiwan
2
2019). The 3rd Generation Partnership Project (3GPP) also has proposed the concept of Local Access Data Network (LADN) as a new paradigm to support MEC in 5G (ETSI TS 2018). By placing MEC servers near to base stations (gNB), MEC is able to provide computation and storage resources to mobile users with low end-to-end latency. IP multicast is another mechanism that could reduce the bandwidth requirement of the backhaul network. There are two mechanisms to impro
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