Zhan Ma

GreenTube: power optimization for mobile videostreaming via dynamic cache management

Mobile video streaming has become one of the most popular applications in the trend of smartphone booming and the prevalence of 3G/4G networks, i.e., HSPA, HSPA+, and LTE. However, the prohibitively high power consumption by 3G/4G radios in smartphones reduces battery life significantly and thus severely hurts user experience. To tackle this challenge, we designed GreenTube, a system that optimizes power consumption for mobile video streaming by judiciously scheduling downloading activities to minimize unnecessary active periods of 3G/4G radio. GreenTube achieves this by dynamically managing the downloading cache based on user viewing history and network condition. We implemented GreenTube on Android-based smartphones. Experimental results show that GreenTube achieves large power reductions of more than 70% (on the 3G/4G radio) and 40% (for the whole system). We believe GreenTube is a desirable upgrade to the Android system, especially in the light of increasing LTE popularity.

De-ringing filter for Scalable Video Coding

This paper reports the recent inter-layer filtering development for the scalable extension of the high-efficiency video coding (HEVC). The essential difference between scalable and single-layer video coding technology is the existence of the inter-layer redundancy, which can be exploited by the interlayer prediction to improve the coding efficiency. One form of inter-layer prediction is to up-sample the reconstructed base-layer picture as a predictor for the enhancement-layer. However, due to the intrinsic filter characteristics during the resampling process and the quantization error in the base-layer, this prediction picture often has low quality, with significant ringing artifacts. This paper proposes an adaptive de-ringing filter to reduce such ringing artifacts. With the reduction of the ringing artifacts, inter-layer prediction can be more effective and coding efficiency can be improved. Simulation result shows that on average from 0.5% to 1.7% and up to 4.6% BD-Rate (Bjontegaard Delta-Rate) saving can be achieved using this single tool for emerging HEVC scalability extension.

Unified complexity model for H.264/AVC video processing on mobile platform

In this paper, a unified computational complexity model is proposed to predict the H.264/AVC encoding and decoding computing cycles on popular ARM featured mobile platform. We have developed an analytical complexity model considering the video spatial resolution (i.e., frame size), temporal resolution (i.e., frame rate), and amplitude resolution (i.e., signal amplitude which is usually controlled by compression quantization parameter (QP)). Our proposed model has been validated for H.264/AVC encoding, where x264 is chosen as the typical mobile H.264 encoder. The same analytical model is also extended and verified for the H.264/AVC decoding using FFmpeg. Extensive simulations have been carried out to experiment different scenarios using various video sources at different frame sizes (e.g., HD to QCIF), frame rates (e.g., 60 to 3.75 fps) and bit rates (either using constant QP or rate control). Results demonstrate the high accuracy of our proposed model, with the average relative prediction error less than 7%.