Scalable video traffic offloading for streaming services in 5G HetNets

In this paper, by combining scalable video coding (SVC) and traffic offloading, we propose a scalable video traffic offloading (SVO) approach to provide video streaming services in 5G heterogeneous networks (HetNet). We aim to maximize the number of users receiving the base layer (BL) of the video and to maximize the mean quality of experience (QoE) of users by increasing the number of received enhancement layers (ELs) of the video. Tao this end, we consider a multi-objective mixed-integer programming problem that associates each user to either a macrocell or femtocell and allocates video layers to the users. We solve the multi-objective problem by using the weighted sum of the two objectives, so we obtain a Pareto-optimal solution. To choose one among the solutions, we pick the maximum resource-efficient (ME) one, which uses the least resource blocks (RBs). As obtaining ME solution is computationally complex, we separate the problem into a cell allocation (CA) and a video layers allocation (VLA) problem and propose a two-step heuristic solution. To obtain the heuristic solution, we take advantage of the video traffic scalability; in the first step, we allocate BLs to cells to increase the number of users receiveing service, and we assign ELs to the users to improve the mean QoE in the second step. We evaluate the heuristic solution by comparing it with the upper bound solution. Simulation results show that a narrow gap exists between the upper bound and the heuristic solution, while it has a low computational complexity which makes it appropriate for streaming services implementation. Furthermore, number of users receiving service in SVO has a high impact on grewing resource efficiency.

Automated summary

This paper presents a scalable video traffic offloading (SVO) approach to improve video streaming services in 5G heterogeneous networks, by combining scalable video coding (SVC) and traffic offloading. Through a thorough investigation of a multi-objective optimization problem, a heuristic solution is proposed and evaluated against an upper bound solution, showing promising performance with low computational complexity for streaming services implementation.