Due to the huge file size of virtual reality (VR) videos, providing high-quality and low-latency live VR videos to multiple users using existing network architectures becomes exceptionally challenging. Although some tile-based multicast schemes can reduce the amount of transmitted data, users with differentiated channel conditions and interaction behaviors still need to be effectively grouped. In this paper, we first model VR video multicast scheduling in the case of orthogonal multiple access (OMA), where the objective of maximizing the sum of user experience qualities forms an NP-hard integer nonlinear programming problem. By analyzing the time complexity source of the exhaustion-based global optimal algorithm and the impact of two factors on grouping, we propose a scheduling algorithm with very low complexity based on dynamic programming in the OMA case and a field-of-view ratio sorting and grouping method for non-orthogonal multiple access (NOMA) scenarios. In extensive actual viewport experiments, the proposed algorithm outperforms the state-of-the-art multicast scheme adopting OMA by 17.86% with negligible time complexity, making it suitable for deployment in realistic environments. The proposed sorting method increases the system utility of NOMA’s optimal solution by 13.13%.

By providing an immersive experience with panoramic views, 360-degree video streaming has gained increasing popularity recently. In many cases, videos are transmitted to mobile users over cellular networks. However, due to the high bandwidth requirement of 360-degree videos and the growing number of users, it is challenging to provide high-quality live streaming services to all users with limited bandwidth. Improving spectral efficiency and reducing bandwidth consumption are two major approaches to address this issue, which can be achieved with non-orthogonal multiple access (NOMA) and scalable video coding (SVC), respectively. In this paper, we apply NOMA and SVC to 360-degree video streaming over a cellular network and propose a multicast scheme called SVCast, aiming to maximize the sum quality of experience of users served by a base station. Such a problem is formulated as an NP-hard problem, and we decompose it into two levels of subproblems. The lower-level subproblem is inter-group spectrum allocation, which is solved by a knapsack approach. The higher-level subproblem is intra-group multicast scheduling, and we propose a recursive algorithm to solve it. Simulation results demonstrate that SVCast improves the system utility by 31.9% on average. Furthermore, SVCast eliminates the need for viewport prediction by aggregating the contents from the viewports of multiple users.

Massive Multiple-Input Multiple-Output (MIMO), with its spatial multiplexing and channel hardening, has the potential to provide high capacity and reliability for massive video services. In spite of this, massive MIMO in the physical layer does not fully showcase its abilities in video applications unless it is specifically designed to do so. In this paper, we consider a cell-free massive MIMO system as an edge node for scheduling massive streams, and thus the standard server-to-client transmission is split into server-to-edge and edge-to-client. When the server-to-edge transmission is ideal, edge schedules massive streams to alleviate user conflicts in cell-free massive MIMO. Moreover, we propose a novel edge-to-client grouping algorithm for assigning the streams with severe interference to different time slots. The proposed algorithm achieves an innovative.7-approximation ratio while keeping the group sizes within a certain range. When the server-to-edge transmission is non-ideal, we design a special transmission framework called Aggressive, wherein the server sends the next video chunk when the current chunk reaches the edge rather than the client. Thus, the proposed framework saves considerable server-to-edge latency compared with the traditional framework. Simulation results show that the proposed user grouping algorithm improves the achievable rate by around 18% and the Aggressive framework improves the average bitrate.