Cooperative Caching Plan of Popular Videos for Mobile Users by Grouping Preferences

Mobile traffic has grown fast in recent years, particularly for delivering popular video clips at anywhere. Based on a combination of Macro Cells and several Small Cells (SC) technologies, HetNets is gaining increasing attention due to the surge demand on high-quality mobile video services. To avoid bottleneck in the limited capacity of backhaul link, Mobile Edge Computing (MEC) is a promising solution, which computing and caching in the mobile edge in such a way that the buffered video can be delivered with less network latency and traffic load. Our goal is to build a cooperative caching plan for serving popular video clips over HetNets under the Joint Transmission (JT) method in MEC environment with least possible backhaul traffic. In the training phase, categorize similar users to clusters and to SCs using the well-known spectral clustering algorithm. Then aggregate the users' requests to be the request profile of the corresponding SCs. Third, share the caching space among cooperated SCs with the help of distributed LT codes. During the serving phase, new coming users will be assigned to appropriate SCs based on similarity between users and SCs. Our simulation results show that the backhaul traffic rate can decrease from 38% to 10% (or 62% to 19%) if cache space is acceptable.

[1]  Baoxin Li,et al.  YouTubeCat: Learning to categorize wild web videos , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Jure Leskovec,et al.  Overlapping community detection at scale: a nonnegative matrix factorization approach , 2013, WSDM.

[3]  Michael Luby,et al.  LT codes , 2002, The 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002. Proceedings..

[4]  Michael Luby,et al.  A digital fountain approach to reliable distribution of bulk data , 1998, SIGCOMM '98.

[5]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Matti Latva-aho,et al.  Content-aware user clustering and caching in wireless small cell networks , 2014, 2014 11th International Symposium on Wireless Communications Systems (ISWCS).

[7]  Michael Zink,et al.  Characteristics of YouTube network traffic at a campus network - Measurements, models, and implications , 2009, Comput. Networks.

[8]  Alexandros G. Dimakis,et al.  FemtoCaching: Wireless Content Delivery Through Distributed Caching Helpers , 2013, IEEE Transactions on Information Theory.

[9]  Zongpeng Li,et al.  Youtube traffic characterization: a view from the edge , 2007, IMC '07.

[10]  Jia-Shung Wang,et al.  Distributed delivery of popular videos over Ultra-dense networks , 2015, 2015 IEEE Symposium on Computers and Communication (ISCC).

[11]  Christina Fragouli,et al.  MicroCast: cooperative video streaming on smartphones , 2013, MOCO.

[12]  Ching-Hsien Hsu,et al.  QoS prediction for service recommendations in mobile edge computing , 2017, J. Parallel Distributed Comput..

[13]  Alexandros G. Dimakis,et al.  FemtoCaching: Wireless video content delivery through distributed caching helpers , 2011, 2012 Proceedings IEEE INFOCOM.

[14]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[15]  Vahab S. Mirrokni,et al.  Large-Scale Community Detection on YouTube for Topic Discovery and Exploration , 2011, ICWSM.