.png){kind=icon}
.png){kind=icon}
Return
QoE-driven optimization for cloud-assisted DASH-based scalable interactive multiview video streaming over wireless network
.png){kind=icon}
PRE
AI
DOI：10.1016/j.image.2017.05.015.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In interactive multiview video streaming (IMVS), the viewers can periodically switch viewpoints. If the captured view is not available at the desired viewpoint, virtual views can be rendered from neighboring coded views using view synthesis techniques. Dynamic adaptive streaming over HTTP (DASH) is a new standard that allows to adjust the quality of video streaming based on the network condition. In this paper, an improved DASH-based IMVS framework is proposed. It has the following characteristics. First, virtual views could be adaptively generated at either the cloud-based server or the client in our scheme, depending on the network condition and the cost of the cloud. Second, scalable video coding (SVC) is used to improve the flexibility in our system. To optimize the Quality of Experience (QoE) of multiple clients in wireless scenario, we develop a cross-layer optimization scheme. We first propose a new cache management method to selectively store the video data according to SVC structure and the clients' requesting condition. Next, a cross-layer scheduling scheme is proposed by considering the video rate adaptation and the wireless resource allocation. The optimization problem is shown to be equivalent to the Multiple Choice Knapsack (MCKP) problem. A dynamic programming method and a low-cost greedy method are developed to solve the problem. Simulations with the NS3 tool demonstrate the advantage of our proposed scheme over the existing approach that always uses client-based view synthesis and single-layer video coding. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Dynamic adaptive streaming over HTTP (DASH)
Interactive multiview video streaming (IMVS)
Quality of experience (QoE)
Cross-layer design
Resource allocation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
S
IF:
2.7
Papers:
2.8K
Citations:
4.2K