Abstract
Owing to increasing consumption of video streams and demand for higher quality content and more advanced displays, future telecommunication networks are expected to outperform current networks in terms of key performance indicators (KPIs). Currently, content delivery networks (CDNs) are used to enhance media availability and delivery performance across the Internet in a cost-effective manner. The proliferation of CDN vendors and business models allows the content provider (CP) to use multiple CDN providers simultaneously. However, extreme concurrency dynamics can affect CDN capacity, causing performance degradation and outages, while overestimated demand affects costs. 5G standardization communities envision advanced network functions executing video analytics to enhance or boost media services. Network accelerators are required to enforce CDN resilience and efficient utilization of CDN assets. In this regard, this study investigates a cost-effective service to dynamically select the CDN for each session and video segment at the Media Server, without any modification to the video streaming pipeline being required. This service performs time series forecasts by employing a Long Short-Term Memory (LSTM) network to process real time measurements coming from connected video players. This service also ensures reliable and cost-effective content delivery through proactive selection of the CDN that fits with performance and business constraints. To this end, the proposed service predicts the number of players that can be served by each CDN at each time; then, it switches the required players between CDNs to keep the (Quality of Service) QoS rates or to reduce the CP's operational expenditure (OPEX). The proposed solution is evaluated by a real server, CDNs, and players and delivering dynamic adaptive streaming over HTTP (MPEG-DASH), where clients are notified to switch to another CDN through a standard MPEG-DASH media presentation description (MPD) update mechanism.
Highlights
I N THE last few years, the demand for video content across the Internet has constantly increased
The training dataset consisted of a multivariate time series, and bandwidth and latency measurements were taken for three hours
The testing dataset consisted of the training dataset with an extra hour of data collected on a different day that was independent of the training dataset
Summary
I N THE last few years, the demand for video content across the Internet has constantly increased. Video streams from professional applications, such as Industrial Internet of Things (IIoT), medical equipment, connected and autonomous cars, and from domestic services, such as gaming, virtual reality, augmented reality, video over IP (VoIP) sports services, and over-the-top (OTT) platforms are flooding networks with real-time data intensive sessions This evolution of Internet traffic makes evident the severity of the network’s capacity to guarantee a certain quality of service (QoS) for the video applications. The popularity of video streaming services over the Internet pushed video industry-Moving Picture Experts Group (MPEG)-and standardization bodies to create new formats which enable adaptive streaming over the already existing Hypertext Transfer Protocol (HTTP) infrastructures They allow the player devices to adapt the content representation to the specific device capabilities (resolution, codecs, etc.) and the changeable network connectivity
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