Content-Sensing Based Resource Allocation forDelay-Sensitive VR Video Uploading in 5G H-CRAN.

Area of Science:

• Telecommunications
• Computer Networks
• Virtual Reality

Background:

• Virtual reality (VR) video uploading is a key application for fifth-generation (5G) networks.
• Heterogeneous cloud-radio access networks (H-CRAN) offer high transmission rates for VR video.
• User equipment mobility and 5G H-CRAN's small cell features present challenges for VR video uploading.

Purpose of the Study:

• To propose a content-sensing based resource allocation scheme for delay-sensitive VR video uploading in 5G H-CRAN.
• To address the challenges posed by user equipment mobility and small cell features in 5G H-CRAN for VR video uploading.
• To optimize resource allocation, including g-NB group resource allocation, RRH/g-NB association, sub-channel assignment, power allocation, and tile encoding rate.

Main Methods:

• Formulated the resource allocation as a mixed-integer nonlinear problem (MINLP).
• Developed a three-stage algorithm: dynamic g-NB group resource allocation, iterative joint RRH/g-NB association, sub-channel, and power allocation, and convex optimization for tile encoding rate assignment.
• Utilized a convex optimization toolbox for final encoding tile rate assignment.

Main Results:

• The proposed algorithm ensures system utility under maximum transmission delay and power constraints.
• Achieved low complexity and faster convergence compared to existing methods.
• Successfully optimized resource allocation for delay-sensitive VR video uploading.

Conclusions:

• The content-sensing resource allocation scheme effectively manages VR video uploading in 5G H-CRAN.
• The proposed algorithm provides a robust solution for optimizing VR video transmission under various constraints.
• Demonstrated the feasibility and efficiency of the approach for future 5G applications.