Publication Date

Spring 2016

Document Type

Project Summary

Degree Name

Master of Science


Computer Science

First Advisor

(Clare) Xueqing Tang, Ph.D.

Second Advisor

Do Young Park, Ph.D.

Third Advisor

Soon-Ok Park, Ph.D.


In this project, we realized a prototype of this architecture to validate the feasibility of the proposed method. According to the experiment, this method could provide efficient self-adaptive multimedia streaming services for varying bandwidth environment. Video streaming is gaining popularity among mobile users. The latest mobile devices, such as smart phones and tablets, are equipped with multiple wireless network interfaces. How to efficiently and cost-effectively utilize multiple links to improve video streaming quality needs investigation. In order to maintain high video streaming quality while reducing the wireless service cost, in this paper, the optimal video streaming process with multiple links is formulated as a Markov Decision Process (MOP). The reward function is designed to consider the quality of service (QoS) requirements for video traffic, such as the startup latency, playback fluency, average playback quality, playback smoothness and wireless service cost. To solve the MOP in real-time, we propose an adaptive, best-action search algorithm to obtain a sub-optimal solution. To evaluate the performance of the proposed adaptation algorithm, we implemented a test bed using the Android mobile phone and the Scalable Video Coding (SVC) codec. Experiment results demonstrate the feasibility and effectiveness of the proposed adaptation algorithm for mobile video streaming applications, which outperforms the existing state-of-the-art adaptation algorithms.


Authors listed in alphabetical order by OPUS staff.