CAME: cloud-assisted motion estimation for mobile video compression and transmission

Video streaming has become one of the most popular networked applications and, with the increased bandwidth and computation power of mobile devices, anywhere and anytime streaming has become a reality. Unfortunately, it remains a challenging task to compress high-quality video in real-time in such devices given the excessive computation and energy demands of compression. On the other hand, transmitting the raw video is simply unaffordable from both energy and bandwidth perspective. In this paper, we propose CAME, a novel cloud-assisted video compression method for mobile devices. CAME leverages the abundant cloud server resources for motion estimation, which is known to be the most computation-intensive step in video compression, accounting for over 90% of the computation time. With CAME, a mobile device selects and uploads only the key information of each picture frame to cloud servers for mesh-based motion estimation, eliminating most of the local computation operations. We develop smart algorithms to identify the key mesh nodes, resulting in minimum distortion and data volume for uploading. Our simulation results demonstrate that CAME saves almost 30% energy for video compression and transmission.

[1]  Chin-Feng Lai,et al.  A personalized mobile IPTV system with seamless video reconstruction algorithm in cloud networks , 2011, Int. J. Commun. Syst..

[2]  Baochun Li,et al.  Quality-assured cloud bandwidth auto-scaling for video-on-demand applications , 2012, 2012 Proceedings IEEE INFOCOM.

[3]  Yao Wang,et al.  Video Processing and Communications , 2001 .

[4]  Indrajit Chakrabarti,et al.  Power efficient motion estimation algorithm and architecture based on pixel truncation , 2011, IEEE Transactions on Consumer Electronics.

[5]  Klara Nahrstedt,et al.  Energy-efficient CPU scheduling for multimedia applications , 2006, TOCS.

[6]  Arun Venkataramani,et al.  Energy consumption in mobile phones: a measurement study and implications for network applications , 2009, IMC '09.

[7]  Chong Luo,et al.  Resource allocation for cloud-based free viewpoint video rendering for mobile phones , 2011, ACM Multimedia.

[8]  Asral Bahari,et al.  Low-Power H.264 Video Compression Architectures for Mobile Communication , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Bo Li,et al.  Novasky: Cinematic-quality VoD in a P2P storage cloud , 2011, 2011 Proceedings IEEE INFOCOM.

[10]  Eduardo Peixoto,et al.  Mobile video communications using a Wyner-Ziv transcoder , 2008, Electronic Imaging.

[11]  Kundan Singh,et al.  Flash-based Audio and Video Communication in the Cloud , 2011, ArXiv.

[12]  Edmund S Jackson,et al.  Video Compression System for Mobile Devices , 2022 .

[13]  Chao Mei,et al.  CloudStream: Delivering high-quality streaming videos through a cloud-based SVC proxy , 2011, 2011 Proceedings IEEE INFOCOM.

[14]  Christian Roux,et al.  Triangular active mesh for motion estimation , 1997, Signal Process. Image Commun..