A cooperative video-streaming transmission strategy in information-centric networks

Hyper-dense network is a promising solution to the high mobile video traffic. However, it has some fundamental problems. To address these issues, we propose a novel information-centric cooperative video transmission strategy in hyper-dense networks via caching. In the proposed scheme, each video file is encoded into segments using maximum distance separable rateless code. Then, a portion of each segment is cached at a certain small-cell base station (SBS), so that the SBSs can cooperatively transmit these segments to users without incurring high payload backhaul. To deal with interference, interference alignment (IA) is adopted, which is combined with the greedy algorithm to transmit video-file segments to these users. Simulation results show that the proposed video transmission scheme can reduce the waiting delay when watching real-time videos and thus improve the quality of experience.

[1]  Syed Ali Jafar,et al.  Interference Alignment and Spatial Degrees of Freedom for the K User Interference Channel , 2007, 2008 IEEE International Conference on Communications.

[2]  Mérouane Debbah,et al.  On the benefits of edge caching for MIMO interference alignment , 2015, 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[3]  Vincent K. N. Lau,et al.  Exploiting Base Station Caching in MIMO Cellular Networks: Opportunistic Cooperation for Video Streaming , 2015, IEEE Transactions on Signal Processing.

[4]  Victor C. M. Leung,et al.  To Align or Not to Align: Topology Management in Asymmetric Interference Networks , 2017, IEEE Transactions on Vehicular Technology.

[5]  Victor C. M. Leung,et al.  Opportunistic communications in interference alignment networks with wireless power transfer , 2015, IEEE Wireless Communications.

[6]  Abdallah Khreishah,et al.  Joint Caching, Routing, and Channel Assignment for Collaborative Small-Cell Cellular Networks , 2016, IEEE Journal on Selected Areas in Communications.

[7]  Xiaofei Wang,et al.  Cache in the air: exploiting content caching and delivery techniques for 5G systems , 2014, IEEE Communications Magazine.

[8]  Nan Zhao,et al.  Adaptive Power Allocation Schemes for Spectrum Sharing in Interference-Alignment-Based Cognitive Radio Networks , 2016, IEEE Transactions on Vehicular Technology.

[9]  Victor C. M. Leung,et al.  Interference Alignment and Its Applications: A Survey, Research Issues, and Challenges , 2016, IEEE Communications Surveys & Tutorials.

[10]  Victor C. M. Leung,et al.  Communications, caching, and computing oriented small cell networks with interference alignment , 2016, IEEE Communications Magazine.

[11]  Wei Yu,et al.  Content-Centric Sparse Multicast Beamforming for Cache-Enabled Cloud RAN , 2015, IEEE Transactions on Wireless Communications.

[12]  F. Richard Yu,et al.  Interference alignment with delayed channel state information and dynamic AR-model channel prediction in wireless networks , 2015, Wirel. Networks.

[13]  Yan Yu,et al.  Power Allocation for Cache-Aided Small-Cell Networks With Limited Backhaul , 2017, IEEE Access.

[14]  Mehdi Bennis,et al.  Living on the edge: The role of proactive caching in 5G wireless networks , 2014, IEEE Communications Magazine.

[15]  A. H. Kayran,et al.  On Feasibility of Interference Alignment in MIMO Interference Networks , 2009, IEEE Transactions on Signal Processing.

[16]  Ying-Chang Liang,et al.  Optimal channel estimation and training design for two-way relay networks , 2009, IEEE Transactions on Communications.