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首页> 外文期刊>Multimedia, IEEE Transactions on >Collaborative Wireless Freeview Video Streaming With Network Coding
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Collaborative Wireless Freeview Video Streaming With Network Coding

机译：网络编码的协作式无线Freeview视频流

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Free viewpoint video (FVV) offers compelling interactive experience by allowing users to switch to any viewing angle at any time. An FVV is composed of a large number of camera-captured anchor views, with virtual views (not captured by any camera) rendered from their nearby anchors using techniques such as depth-image-based rendering (DIBR). We consider a group of wireless users who may interact with an FVV by independently switching views. We study a novel live FVV streaming network where each user pulls a subset of anchors from the server via a primary channel. To enhance anchor availability at each user, a user generates network-coded (NC) packets using some of its anchors and broadcasts them to its direct neighbors via a secondary channel. Given limited primary and secondary channel bandwidths at the devices, we seek to maximize the received video quality (i.e., minimize distortion) by optimizing the set of anchors each device pulls and the anchor combination to generate NC packets. To our best knowledge, this is among addressing such joint optimization problem for wireless live FVV streaming with NC-based collaboration. We first formulate the problem and show that it is NP-hard. We then propose a scalable and effective algorithm called PAFV (Peer-Assisted Freeview Video). In PAFV, each node collaboratively and distributedly decides on the anchors to pull and NC packets to share so as to minimize video distortion in its neighborhood. Extensive simulation studies show that PAFV outperforms other algorithms, achieving substantially lower video distortion (often by more than 20–50%) with significantly less redundancy (by as much as 70%). Our Android-based video experiment further confirms the effectiveness of PAFV over comparison schemes.
机译：自由视点视频（FVV）通过允许用户随时切换到任意视角来提供引人入胜的互动体验。 FVV由大量相机捕获的锚点视图组成，并使用基于深度图像的渲染（DIBR）等技术从其附近的锚点渲染虚拟视图（未由任何相机捕获）。我们考虑了一组可以通过独立切换视图与FVV进行交互的无线用户。我们研究了一种新颖的实时FVV流网络，其中每个用户都通过主要渠道从服务器中提取锚点的子集。为了增强每个用户的锚点可用性，用户使用其一些锚点生成网络编码（NC）数据包，并通过辅助信道将其广播到其直接邻居。给定设备上的主要和次要通道带宽有限，我们试图通过优化每个设备拉出的锚点集和锚点组合以生成NC数据包，来最大程度地提高接收的视频质量（即，最小化失真）。据我们所知，这是通过基于NC的协作解决无线实时FVV流的联合优化问题的方法之一。我们首先提出问题，并证明它是NP难的。然后，我们提出了一种可扩展且有效的算法，称为PAFV（对等辅助的Freeview视频）。在PAFV中，每个节点协同并分布式地决定要拉的锚点和要共享的NC数据包，以最大程度地减少其附近的视频失真。大量的仿真研究表明，PAFV的性能优于其他算法，其视频失真大大降低（通常超过20％到50％），而冗余却少得多（多达70％）。我们基于Android的视频实验进一步证实了PAFV相对于比较方案的有效性。

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