D2D-Enabled Collaborative Edge Caching and Processing with Adaptive Mobile Video Streaming

Mehrabi, Abbas; Siekkinen, Matti; Illahi, Gazi; Ylä-Jääski, Antti

doi:10.1109/wowmom.2019.8792981

Cited by 9 publications

(5 citation statements)

References 26 publications

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“…Mehrabi et al [22] proposed to use MEC for DASH services. In References [20,21], the same authors extended the work with edge caching and device-to-device communication to optimize jointly users' QoE and network traffic.…”

Section: Related Workmentioning

confidence: 99%

“…To make the optimization feasible, we define 10 discrete bitrates (and qualities) for the real-time video streamed to the clients: R = {10, 12,15,20,22,25,28,30,32,35 Mbps}. The viewport size is bound between 1 ≤ r ≤ 1.5 and the upper bound of the latency is set to t max = 0.2s.…”

Section: Experiments Setupmentioning

confidence: 99%

“…Multi-access edge computing (MEC) aims to transfer the computing and communication resources from a far-away central sever to the network edges near the end consumers [13]. Mobile adaptive video streaming in conjunction with MEC technology has been investigated recently [20][21][22]. This prior work has focused on quality of experience (QoE) optimization of video on demand (VOD) services that stream static two-dimensional (2D) video content from either a further-away cloud or nearer edge server to mobile clients.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Tier CloudVR

Mehrabi

Siekkinen

Kämäräinen

et al. 2021

ACM Trans. Multimedia Comput. Commun. Appl.

Self Cite

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The availability of high bandwidth with low-latency communication in 5G mobile networks enables remote rendered real-time virtual reality (VR) applications. Remote rendering of VR graphics in a cloud removes the need for local personal computer for graphics rendering and augments weak graphics processing unit capacity of stand-alone VR headsets. However, to prevent the added network latency of remote rendering from ruining user experience, rendering a locally navigable viewport that is larger than the field of view of the HMD is necessary. The size of the viewport required depends on latency: Longer latency requires rendering a larger viewport and streaming more content. In this article, we aim to utilize multi-access edge computing to assist the backend cloud in such remote rendered interactive VR. Given the dependency between latency and amount and quality of the content streamed, our objective is to jointly optimize the tradeoff between average video quality and delivery latency. Formulating the problem as mixed integer nonlinear programming, we leverage the interpolation between client’s field of view frame size and overall latency to convert the problem to integer nonlinear programming model and then design efficient online algorithms to solve it. The results of our simulations supplemented by real-world user data reveal that enabling a desired balance between video quality and latency, our algorithm particularly achieves the improvements of on average about 22% and 12% in term of video delivery latency and 8% in term of video quality compared to respectively order-of-arrival, threshold-based, and random-location strategies.

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Section: Related Workmentioning

confidence: 99%

Section: Experiments Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Tier CloudVR

Mehrabi

Siekkinen

Kämäräinen

et al. 2021

ACM Trans. Multimedia Comput. Commun. Appl.

Self Cite

View full text Add to dashboard Cite

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“…In order to adjust the video user experience, Reference [20] proposed an adaptive D2D video stream quality-of-service-aware channel resource matching scheme. Reference [21] analyzes how the combination of D2D and edge computing affects the QoE of video streaming clients and helps to reduce backhaul traffic. In Reference [22], a pre-caching algorithm based on QoE gain is proposed to realize the distribution of D2D to VR video.…”

Section: D2d Transmission Technologymentioning

confidence: 99%

Resource allocation for virtual reality content sharing based on 5G D2D multicast communication

Yang

Feng

Zhang

et al. 2020

J Wireless Com Network

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As the development of wireless virtual reality (VR), a great disparity exists among the huge content transmission, rigorous QoS guarantees and the limited bandwidth of cellular networks. With the increasing of cache capacity on user devices, direct content sharing between user devices is a promising solution to solve this problem. To meet the quality of service (QoS) requirement of wireless VR transmission, this paper proposes a content sharing scheme based on 5G device-to-device (D2D) Multicast Communication. In this way, some adjacent VR users can form multicasting clustering to share VR content by working on D2D communication mode. The VR D2D multicasting clusters reuse the uplink channel resource of ordinary VR cellular users in the same cell. The basic prerequisite is that the degrading on QoS of these ordinary VR cellular users can be affordable. We design a two-step scheme to solve this radio resource allocation problem for VR content sharing. Firstly, we find out the optimal transmitting power for each VR user devices by geometric proximity, which metrics are affected by wireless VR throughput, tracking accuracy and delay. In the second step, we transform the channel allocation problem into a bipartite graph matching problem based on the transmitting power metrics, which is optimally solved by the Hungarian algorithm. The simulation results show that the VR content sharing based on 5G D2D communication technology can achieve larger system throughput gain and lower transmission delay. Compared with heuristic scheme and stochastic scheme, the proposed scheme can increase the throughput of the overall network by about 50% and 12%, respectively.

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“…Reference [17] solved a joint optimization problem of caching and transcoding for video on demand (VoD) services in MEC systems. MEC is also applied to adaptive video streaming in [18], and authors explored how to jointly maximize quality of experience (QoE) and minimize backhaul traffic for device-to-device communication.…”

Section: Related Workmentioning

confidence: 99%

Deep-Q-Network-Based Multimedia Multi-Service QoS Optimization for Mobile Edge Computing Systems

et al. 2019

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By offloading storage and computing resources to the edge of networks, mobile edge computing (MEC) is emerged as a promising architecture to reduce the transmission delay and bandwidth waste for mobile multimedia services. This paper focuses on a multi-service scenario in the MEC systems, where the MEC server can provide three multimedia services including live streaming, buffered streaming and low latency enhanced mobile broadband applications for edge users at the same time. In order to satisfy various quality of service (QoS) requirements for different multimedia applications, the 5G QoS model is applied. Notably, the packets from the multimedia applications with the same or similar requirements are mapped into the same QoS flow, and each QoS flow is processed individually. Therefore, how to effectively schedule the limited radio resource for QoS flows is an intractable problem. To address the problem above, a QoS evaluation model is designed, and a QoS maximization problem is formulated. Furthermore, a deep reinforcement learning method, deep-Q-network, is adopted to make decisions to allocate radio resource dynamically. Compared with round-robin and priority-based scheduling algorithms, the simulation results validate that the proposed algorithm outperforms other resource scheduling algorithms for multi-service scenario. INDEX TERMS Deep-Q-network, deep reinforcement learning, mobile edge computing, multimedia, quality of service.

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D2D-Enabled Collaborative Edge Caching and Processing with Adaptive Mobile Video Streaming

Cited by 9 publications

References 26 publications

Multi-Tier CloudVR

Multi-Tier CloudVR

Resource allocation for virtual reality content sharing based on 5G D2D multicast communication

Deep-Q-Network-Based Multimedia Multi-Service QoS Optimization for Mobile Edge Computing Systems

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