Insights from Analysis of Video Streaming Data to Improve Resource Management

Today a large portion of Internet traffic is video. Over The Top (OTT) service providers offer video streaming services by creating a large distributed cloud network on top of a physical infrastructure owned by multiple entities. Our study explores insights from video streaming activity by analyzing data collected from Korea's largest OTT service provider. Our analysis of nationwide data shows interesting characteristics of video streaming such as correlation between user profile information (e.g., age, sex) and viewing habits, viewing habits of users (when do the users watch? using which devices?), viewing patterns (early leaving viewer vs. steady viewer), etc. Video on Demand (VoD) streaming involves costly (and often limited) compute, storage, and network resources. Findings from our study will be beneficial for OTTs, Content Delivery Networks (CDNs), Internet Service Providers (ISPs), and Carrier Network Operators, to improve their resource allocation and management techniques.Comment: This is a preprint electronic version of the article accepted to IEEE CloudNet 201

Characterizing and Modeling User Behavior in a Large-scale Mobile Live Streaming System

International audienceAbstract:In mobile live streaming systems, user are fairly limited in interaction with the streaming objects due to the constraints coming from mobile devices and event-driven nature of live content. The constraints could lead to unique user behavior characteristics, which have yet to be explored. This paper investigates over 9 million access logs collected from the PPTV live streaming system, with an emphasis on the discrepancies that might exist when users access the live streaming catalog from mobile and non-mobile terminals. We observe a much higher likelihood of abandoning sessions by mobile users, and examine the structure of abandoned sessions from the perspectives of time of day, channel content and mobile device types. Surprisingly, we find relatively low abandonment rates during peak-load time periods and a notable impact of mobile device type (i.e. Android or iOS) on the abandonment behavior. To further capture the intrinsic characteristics of user behavior, we develop a series of models for session duration, user activity and time-dynamics of user arrivals/departures. More importantly, we relate the model parameters to physical and real-life meanings. The observations and models shed light on video delivery system, telco-CDNs and mobile applications