3.8 Proceedings Paper

MCOM-Live: A Multi-Codec Optimization Model at the Edge for Live Streaming

Journal

MULTIMEDIA MODELING, MMM 2023, PT II
Volume 13834, Issue -, Pages 252-264

Publisher

SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-031-27818-1_21

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HTTP Adaptive Streaming (HAS) is the predominant technique for delivering video contents across the Internet. This paper proposes a Mixed-Binary Linear Programming (MBLP) model called MCOM-Live for jointly optimizing streaming costs and visual quality by enabling multi-codec content delivery. Experimental results show that our method can reduce latency by up to 23%, streaming costs by up to 78%, and improve visual quality by up to 0.5 dB in terms of PSNR.
HTTP Adaptive Streaming (HAS) is the predominant technique to deliver video contents across the Internet with the increasing demand of its applications. With the evolution of videos to deliver more immersive experiences, such as their evolution in resolution and framerate, highly efficient video compression schemes are required to ease the burden on the delivery process. While AVC/H.264 still represents the most adopted codec, we are experiencing an increase in the usage of new generation codecs (HEVC/H.265, VP9, AV1, VVC/H.266, etc.). Compared to AVC/H.264, these codecs can either achieve the same quality besides a bitrate reduction or improve the quality while targeting the same bitrate. In this paper, we propose a Mixed-Binary Linear Programming (MBLP) model called Multi-Codec Optimization Model at the edge for Live streaming (MCOM-Live) to jointly optimize (i) the overall streaming costs, and (ii) the visual quality of the content played out by the end-users by efficiently enabling multi-codec content delivery. Given a video content encoded with multiple codecs according to a fixed bitrate ladder, the model will choose among three available policies, i.e., fetch, transcode, or skip, the best option to handle the representations. We compare the proposed model with traditional approaches used in the industry. The experimental results show that our proposed method can reduce the additional latency by up to 23% and the streaming costs by up to 78%, besides improving the visual quality of the delivered segments by up to 0.5 dB, in terms of PSNR.

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