Methods for Adaptive Video Streaming and Picture Quality Assessment to Improve QoS/QoE Performances

Abstract

Kenji KANAI †a) , Member, Bo WEI †b) , Zhengxue CHENG †c) , Student Members, Masaru TAKEUCHI †d) , Member, and Jiro KATTO †e) , Fellow SUMMARY This paper introduces recent trends in video streaming and four methods proposed by the authors for video streaming. Video traffic dominates the Internet as seen in current trends, and new visual contents such as UHD and 360-degree movies are being delivered. MPEG-DASH has become popular for adaptive video streaming, and machine learning techniques are being introduced in several parts of video streaming. Along with these research trends, the authors also tried four methods: route navigation, throughput prediction, image quality assessment, and perceptual video streaming. These methods contribute to improving QoS/QoE performance and reducing power consumption and storage size.

Evaluation of Throughput Prediction for Adaptive Bitrate Control Using Trace-Based Emulation

“…The default throughput estimation method installed in the DASH client is exponential weighted moving average (EWMA) [9]. In our previous works [15], [16], [24], we have proposed and implemented several methods of prediction. They are the hybrid model of the autoregressive model and HMM (Hybrid) [15] and throughput prediction based on LSTM (TRUST) [16].…”