Journal of Real-Time Image Processing

, Volume 12, Issue 2, pp 329–342 | Cite as

A DASH-based HEVC multi-view video streaming system

  • Tianyu SuEmail author
  • Ashkan Sobhani
  • Abdulsalam Yassine
  • Shervin Shirmohammadi
  • Abbas Javadtalab
Special Issue Paper


Recent advancement in cameras and image processing technology has generated a paradigm shift from traditional 2D and 3D video to multi-view video (MVV) technology, while at the same time improving video quality and compression through standards such as high efficiency video coding (HEVC). In multi-view, cameras are placed in predetermined positions to capture the video from various views. Delivering such views with high quality over the Internet is a challenging prospect, as MVV traffic is several times larger than traditional video, since it consists of multiple video sequences, each captured from a different angle, requiring more bandwidth than single-view video to transmit MVV. In addition, the Internet is known to be prone to packet loss, delay, and bandwidth variation, which adversely affect MVV transmission. Another challenge is that end users’ devices have different capabilities in terms of computing power, display, and access link capacity, requiring MVV to be adapted to each user’s context. In this paper, we propose an HEVC multi-view system using Dynamic Adaptive Streaming over HTTP to overcome the above-mentioned challenges. Our system uses an adaptive mechanism to adjust the video bit rate to the variations of bandwidth in best effort networks. We also propose a novel scalable way for the multi-view video and depth content for 3D video in terms of the number of transmitted views. Our objective measurements show that our method of transmitting MVV content can maximize the perceptual quality of virtual views after the rendering and hence increase the user’s quality of experience.


High efficiency video coding (HEVC) Dynamic Adaptive Streaming over HTTP (DASH) Multi-view plus depth coding (MVD) Multi-view video coding video scalability and adaptation Quality of experience Depth image-based rendering Rate-distortion optimization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Distributed and Collaborative Virtual Environments Research Laboratory (DISCOVER)University of OttawaOttawaCanada

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