lte -Live:在直播流的边缘轻量级转码

A. Erfanian, Hadi Amirpour, F. Tashtarian, C. Timmerer, H. Hellwagner
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引用次数: 9

摘要

视频直播在视频服务中被广泛接受,其应用近年来备受关注。要求高质量(例如4K分辨率)直播视频的用户数量的增加增加了回程网络的带宽利用率。为了降低HTTP自适应流(HAS)中的带宽利用率,在动态转码方法中,只有最高比特率表示被传递到边缘,其他表示由边缘转码生成。然而,这种方法由于高的转码成本而效率低下。在本文中,我们提出了一种用于实时应用的轻量级边缘转码方法,lte - live,以降低带宽利用率和整体直播成本。在原始服务器的编码过程中,最优编码决策被保存为元数据,元数据替换比特率阶梯中的相应表示。与元数据的相应表示形式相比,元数据的大小显着减小,从而降低了带宽利用率。然后在边缘使用提取的元数据来减少转码时间。我们将问题表述为混合二进制线性规划(MBLP)模型,以优化直播成本,包括带宽和计算成本。我们将所提出的模型与现有的方法进行了比较,实验结果表明,所提出的方法分别节省了34%和45%的成本和回程带宽利用率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LwTE-Live: Light-weight Transcoding at the Edge for Live Streaming
Live video streaming is widely embraced in video services, and its applications have attracted much attention in recent years. The increased number of users demanding high quality (e.g., 4K resolution) live videos increases the bandwidth utilization in the backhaul network. To decrease bandwidth utilization in HTTP Adaptive Streaming (HAS), in on-the-fly transcoding approaches, only the highest bitrate representation is delivered to the edge, and other representations are generated by transcoding at the edge. However, this approach is inefficient due to the high transcoding cost. In this paper, we propose a light-weight transcoding at the edge method for live applications, LwTE-Live, to decrease the bandwidth utilization and the overall live streaming cost. During the encoding processes at the origin server, the optimal encoding decisions are saved as metadata and the metadata replaces the corresponding representation in the bitrate ladder. The significantly reduced size of the metadata compared to its corresponding representation decreases the bandwidth utilization. The extracted metadata is then utilized at the edge to decrease the transcoding time. We formulate the problem as a Mixed-Binary Linear Programming (MBLP) model to optimize the live streaming cost, including the bandwidth and computation costs. We compare the proposed model with state-of-the-art approaches, and the experimental results show that our proposed method saves the cost and backhaul bandwidth utilization up to 34% and 45%, respectively.
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