The resulted data were obtained by implementing the proposed architecture on Ubuntu 16.04 installed in an HP Z230 tower workstation with an Intel Xeon processor and 16 GB RAM. Mininet emulator is used to run the infrastructure plane, which includes the SDN-enabled video streaming network. RYU Controller utilised to emulate the control plane, which collects information about network topology to obtain the environment states. Three realistic network topologies are used for the experimental evaluation of our approach; a modified Abilene topology, Geant, and Cernet. The topologies have been built and implemented in Mininet using a Python script; SDN-Openflow switches replaced the nodes for each network topology. Each switch has a host that forwards and receives different types of traffic. Multimedia providers are deployed in a number of Openflow switches. The provider is able to stream real-time Dynamic Adaptive Streaming over HTTP (DASH) based video flows. DASH video is divided into 4s chunks encoded into five discrete bit rates ranging from 260 Kbps to 2998 Kbps using FFmpeg version 4.3.2 with the H.264 codec, and segmented based on GPAC MP4Box in order to create the DASH manifest and associated files. The video content streamed by multimedia providers is the “Big Buck Bunny” animation with a 1920 × 1080 pixels resolution and was cut into 5 min long. The selection of hosts that partake in the experiment has been constructed to enable the traffic flow to pass through the whole network topology. In the meantime, the study has utilised Wireshark as video traffic monitoring software in the end user's device in order to capture the received video segments during video streaming. The OSPF-based approach is compared with our solution to evaluate customers' desired satisfaction across real-time DASH video streaming. The provided files present the performance comparison of the proposed RL-based solution with the OSPF protocol in terms of the customers’ satisfaction represented by Multimethod Assessment Fusion (VMAF) and Structural Similarity Index Metric (SSIM), and network throughput.
|Date made available||4 Aug 2022|
|Publisher||University of Northampton|
|Date of data production||15 Jun 2022 - 22 Jun 2022|