Poster:Inferring Video Resolution with Coarse QoS in GEO Satellite Networks

Video streaming is a dominant force in today's network landscape, representing 53% of total network traffic. This prominence makes it essential for Internet Service Providers (ISPs) to monitor the quality of experience (QoE) of video streaming for their users to ensure a satisfactory service. However, ISPs face a significant challenge in this regard because the payload data of video streams is encrypted. To address this, recent studies have suggested the use of fine-granularity network information like TCP headers and per-second network throughput of individual video streams as features to estimate QoE using machine learning[1, 2]. While these methods are promising, they present challenges in terms of storage cost and complexity. Our goal in this work is to evaluate the feasibility of performing video resolution inference with coarse granularity network features on a production Geosynchronous (GEO) satellite network.