QoE management in DASH systems using the segment aware rate adaptation algorithm

Dynamic Adaptive Streaming over HTTP (DASH) enables the video player to adapt the bitrate of the video while streaming to ensure playback without interruptions even with varying throughput. A DASH server hosts multiple representations of the same video, each of which is broken down into small segments of fixed playback duration. The video bitrate adaptation is purely driven by the player at the endhost. Typically, the player employs an Adaptive Bitrate (ABR) algorithm, that determines the most appropriate representation for the next segment to be downloaded, based on the current network conditions and user preferences. The aim of an ABR algorithm is to dynamically manage the Quality of Experience (QoE) of the user during the playback. ABR algorithms manage the QoE by maximizing the bitrate while at the same time trying to minimize the other QoE metrics: playback start time, duration and number of buffering events, and the number of bitrate switching events. Typically, the ABR algorithms manage the QoE by using the measured network throughput and buffer occupancy to adapt the playback bitrate. However, due to the video encoding schemes employed, the sizes of the individual segments may vary significantly. For low bandwidth networks, fluctuation in the segment sizes results in inaccurate estimation the expected segment fetch times, thereby resulting in inaccurate estimation of the optimum bitrate. In this paper we demonstrate how the Segment-Aware Rate Adaptation (SARA) algorithm, that considers the measured throughput, buffer occupancy, and the variation in segment sizes helps in better management of the users' QoE in a DASH system. By comparing with a typical throughput-based and buffer-based adaptation algorithm under varying network conditions, we demonstrate that SARA manages the QoE better, especially in a low bandwidth network. We also developed AStream, an open-source Python-based emulated DASH-video player that was used to evaluate three different ABR algorithms and measure the QoE metrics with each of them.

I. Introduction

HTTP based video streaming traffic has been consistently dominating Internet traffic over the last few years. In North America, Netflix and YouTube accounted for 43% of the peak-hour download traffic in 2014 [1]. If we consider other Video-On-Demand (VOD) services such as Hulu and Amazon Video, this percentage rises to as high as 63%. The popularity of HTTP based video streaming is primarily due to the following reasons: HTTP is a stateless protocol unlike other streaming protocols such as Real-Time streaming Protocol (RTSP) and Real-Time Messaging Protocol (RTMP). With HTTP-based streaming, the service providers can use existing web-servers, caches and content distribution networks. HTTP also enables easy access through Network Address Translation (NAT) devices and firewalls.