Measurement and Modeling of Video Watching Time in a Large-Scale Internet Video-on-Demand System

Abstract

Video watching time is a crucial measure for studying user watching behavior in online Internet video-on-demand (VoD) systems. It is important for system planning, user engagement understanding, and system quality evaluation. However, due to the limited access of user data in large-scale streaming systems, a systematic measurement, analysis, and modeling of video watching time is still missing. In this paper, we measure PPLive, one of the most popular commercial Internet VoD systems in China, over a three week period. We collect accurate user watching data of more than 100 million streaming sessions of more than 100 thousand distinct videos. Based on the measurement data, we characterize the distribution of watching time of different types of videos and reveal a number of interesting characteristics regarding the relation between video watching time and various video-related features (including video type, duration, and popularity). We further build a suite of mathematical models for characterizing these relationships. Extensive performance evaluation shows the high accuracy of these models as compared with commonly used data-mining based models. Our measurement and modeling results bring forth important insights for simulation, design, deployment, and evaluation of Internet VoD systems.

Proactive Video Chunks Caching and Processing for Latency and Cost Minimization in Edge Networks

“…We consider that a video chunk v l i can be obtained by transcoding the video chunk v h i , if l ≤ h, Fig. 1: Illustration of the proposed collaborative chunks caching and processing system: (1) The chunk can be served from home server; (2) The chunk can be served from the home server after being transcoded; (2) The chunk can be served from the neighbor server; (3) The chunk can also be served and transcoded in the neighbor server; (4) The chunk can be served from the neighbor BS server and transcoded at the home server; (6)- (7) The chunk can be served from the cloud either directly or after being transcoded loacally.…”

“…However, in real life scenarios, video content is usually partitioned into small chunks of few seconds which can be transferred independently. Also, viewers only watch small parts of a video before leaving the session as stated in [6] and [7]. This makes requesting the whole video from the CDN a waste in terms of cost and delivery latency.…”

“…This leads to a rapid cache storage saturation and a lower caching efficiency because of storing a long video in the same base station; (b) a lower processing efficiency and a higher transcoding cost caused by generating videos that will not be fully watched, in addition to a rapid resource consumption because of transcoding a long video in one server. In the same context, authors in [7] showed that the watching time of videos can be studied since it is impacted by the length, the popularity and the category of a video. Hence, based on these challenges, we propose to derive a study of chunks popularity based on users viewing pattern.…”

EQ-Video: Energy and Quota-Aware Video Playback Time Maximization for Smartphones

“…Meanwhile, a recent study reveals that smartphone users tend to watch only about 60% of online video clips to the end [4]. Such tendency results in undesirable waste of data and energy for the following reason.…”

“…In the proposed algorithm, we adopt the statistics from [4] cost function in terms of both data usage and energy consumption while incorporating expected data and energy waste. By minimizing the cost, we can selectively save more depleting resource as much as possible by monitoring their current status.…”