QoE-Aware Dynamic Resource Management in Future Softwarized and Virtualized Networks

With the advent of multimedia streaming services such as 4K/8K and extended reality, managing and optimizing multimedia service delivery to end-users remains a challenge for mobile network operators, Internet service providers, and over-the-top (OTT) providers. To solve the present concerns and challenges associated with multimedia traffic management and quality of experience (QoE) in next-generation networks, new network and service management approaches for multimedia services are required. Network softwarization and virtualization paradigms that leverage software defined networking (SDN) and network function virtualization (NFV) are considered as key technologies for network and service management in future softwarized networks. This paper adopts SDN control logic to implement a dynamic QoE-aware resource management approach in future softwarized and virtualized networks. We propose QoESoft, a QoE-aware network softwarization approach for multimedia streaming services that (a) performs a dynamic link and switch resources management, and (b) improves end-user’ QoE in softwarized systems. We introduce two important components, the QoE-sdnFlow monitor and QoE-sdnFlow manager as an extension to the SDN controller to monitor and manage the overall utilization of resources of mapped virtual links and nodes. We present a practical implementation of the QoESoft approach using a dash.js reference player where the bitrate decision adaptation logic and video segment download process are modified by introducing two components: the
$BandwidthPredictor$
and the
$Reporting$
functions. The
$BandwidthPredictor$
component ensures that the video bitrate is selected based on the available resources on the client’s side in a dynamic manner. The
$Reporting$
module provides the current streaming status of the dash.js player (e.g., stall duration, bitrate switching, startup delay, and video QoE). In addition, we present the initial evaluations of virtual network survivability and an economical analysis that maximizes the profit for OTTs/ISPs by providing better QoE to customers. The performance of the proposed approach is analyzed through extensive experiments using a Laboratory for Image and Video Engineering - Amazon Prime Video (LIVE-APV) Streaming Database running over the developed softwarized - DASH- based platform based on the Mininet and POX controller. Preliminary results indicate that QoESoft outperforms the baseline approach in terms of link resources utilization and switch resources utilization, low-live latency, startup delays, bitrate switching, stall duration and video QoE.