Design and Implementation of Bitrate Adaptation Schemes for Power Capping in Wi-Fi Video Streaming

Gyuwhan Kim,Minseok Song,Dayoung Lee

doi:10.1109/access.2020.2969696

Gyuwhan Kim, Minseok Song + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.2969696

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Abstract

In dynamically adaptive streaming over HTTP (DASH), which is the de facto standard for streaming, each video is divided into segments, and each segment is further transcoded into multiple bitrate versions. This allows a client device to select the most appropriate bitrate version that matches the network bandwidth to avoid jitters or stalls. However, Wi-Fi download of a high-bitrate version may consume significant energy, especially when network conditions are good. To address this, we propose a new streaming method that limits the energy consumed by mobile devices but maintains an acceptable video quality. First, we derive a power model to analyze how bitrate selection affects power consumption in smartphones. Based on this, we propose two algorithms that determine the bitrate of each segment with the aim of maximizing overall video quality while limiting energy consumption. We use dynamic programming and heuristics to address the tradeoff between algorithm complexity and video quality. The proposed scheme was implemented on an Android-based DASH streaming platform, and various issues were resolved to cope with varying network conditions. Experimental results demonstrated that our scheme effectively optimized the video quality while limiting the energy consumption. For example: 1) our scheme uses 4% and 10% less power than DASH while maintaining an excellent video quality, and 2) the average difference between estimated and actual power consumption is 0.8%, thus keeping a precise energy bound.

Highlights

With recent advances in wireless networks, people can access video streaming services anytime anywhere using their smartphones
To adapt video streaming to varied wireless network conditions, dynamically adaptive streaming over HTTP (DASH) techniques have been widely adopted by major streaming companies, including Netflix, Hulu, and YouTube [1]
3) Algorithm development: We propose two bitrate selection algorithms to solve the optimization problem using dynamic programming and heuristics to address the tradeoff between complexity and video quality

Summary

Introduction

With recent advances in wireless networks, people can access video streaming services anytime anywhere using their smartphones. Each video is divided into segments, and each segment is transcoded into various bitrate versions This allows each client device to choose the most appropriate bitrate version to match the network conditions, providing reliable streaming even with varied network conditions [1]. In a DASH environment, bitrate selection is crucial to provide reliable streaming over various network conditions [2]. Three methods of bitrate selection exist in the literature: throughput-based, buffer-based, and hybrid algorithms [2]. To determine the bitrate of the segment, the throughputbased schemes estimate network throughput by monitoring network bandwidth [3], while the buffer-based schemes check the current buffer level [4]. The hybrid schemes use data on the current buffer level and network throughput to take advantage of both schemes [5]

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