Paving the Way for Efficient Content Delivery in Mobile Networks

Abstract

The flexibility of future mobile networks exploiting modern technologies such as cloud-optimized radio access and software-defined networks opens a gateway to deploying dynamic strategies for live and on-demand content delivery. Traditional live broadcasting systems are spectral inefficient. It takes up a lot more radio spectrum than that of mobile networks, to cover the same size of an area. Furthermore, content caching at base stations reduces network traffic in core networks. However, numerous duplicated copies of contents are still transmitted in the unicast fashion in radio access networks. It consumes valuable radio spectrum and unnecessary energy. Finally, due to the present of numerous mobile receivers with a wide diversity of wireless channels in a base station coverage area, it is a challenge to select a proper modulation scheme for video broadcasting to optimize the quality of services for users. In this thesis, the challenges and the problems in the current strategies for content delivery are addressed. A holistic novel solution is proposed that considers user preferences, user mobility, device-to-device communication, physical-layer resource allocation, and video quality prediction. First, a system-level scheduling framework is introduced to increase the spectral efficiency on broadcasting live contents onto mobile networks. It considers the audience preferences for allocating radio resources spatially and temporally. Second, to reduce the redundant transmissions in radio access networks, a content distribution system that exploits user mobility is proposed that utilizes the urban-scale user mobility and broadcasting nature of wireless communication for delay-tolerant large size content. Third, to further reduce the energy consumption in network infrastructure, a content distribution system that relies on both user mobility, and device-to-device communication is proposed. It leverages the mobile users as content carriers to offload the heavy mobile traffic from network-level onto device-level. Fourth, to mitigate the multi-user channel diversity problem, a cross-layer approach is deployed to increase the video quality for users especially for those who have a low signal-to-noise ratio signal. Finally, data mining techniques are employed to predict video qualities of wireless transmissions over mobile networks. The holistic solution has been empirically developed and evaluated. It achieves high spectral and energy efficiency and mitigates the video quality degradation in mobile networks.