Multi-Access Edge Computing-Assisted D2D Streaming for Proximity-Based Social Networking

The mobile data traffic produced by live streaming is expected to see an exploding growth in recent years. To offload live streaming traffic from mobile networks for proximity-based social networking, device-to-device (D2D) communication is considered a promising technology. Unfortunately, the D2D discovery process requires constant signaling and is energy-consuming. One way to improve this drawback of D2D discovery is to exploit the ETSI multi-access edge computing (MEC) technology, where the knowledge of devices in proximity can be maintained in an MEC server and used to simplify the discovery procedure. In this paper, we aim to implement an MEC-assisted D2D live streaming service that offloads data traffic from mobile networks hierarchically with MEC and D2D communication. Specifically, the service includes an MEC App and a User App. The MEC APP can assist D2D discovery and cache live streams in the MEC server to reduce network latency. The User App can establish D2D connections between devices and share live streams over a D2D network using Wi-Fi Direct, offloading data traffic from mobile networks. We further design a rate adaptation heuristic that is capable of determining a suitable quality level in adaptive streaming for a multi-hop D2D network to improve the overall quality of experience (QoE). The experiment results justify that our proposed architecture and rate adaptation heuristic can provide improved network performance and user QoE for the mobile live streaming service.