| Summary: | 碩士 === 國立交通大學 === 電機學院電信學程 === 105 === Video streaming is one of the most popular real-time Internet services which has changed the viewing behaviors of millions of people around the world. A novel streaming technique called adaptive bitrate streaming (ABS) that allocates appropriate video bitrates based on the current overall network capacity (especially CPU and memory) and traffic condition can minimize the buffering requirement and provide satisfactory user experiences to all viewers.
By monitoring the network condition and evaluating the available bandwidth continuously, an ABS client adaptively selects an appropriate video bitrate. However, conventional ABS techniques endow each individual client with full authority to determine the desired bandwidth. Each client thus unilaterally observes the network traffic and makes a video bitrate decision which serves its demand best. The distributed decisions entail unfairness in bandwidth allocation as ABS clients tend to overestimate the required bandwidth. As a result, some may be allocated more bandwidth than actually needed while others receive less than they really need. Furthermore, an overestimation may cause an underestimation in the next iteration, ultimately bringing the clients with unstable video bitrates and poor quality of experience (QoE).
To evaluate QoE in video streaming services, video quality is often used as a proper metric. Thus, optimizing QoE is equivalent to optimizing the “video quality” fairness. We formulate the QoE optimization problem as a maximum minimum fairness (MMF) problem. It essentially searches for a candidate bandwidth allocation (in bitrate) and the corresponded video quality for all the clients so that the worst client video quality fairness is maximized. Three schemes are proposed to solve the MMF problem. On the other hand, for these solutions to be implementable, the information regarding the network conditions of all clients should be available to a resource allocation agent. It is clear this is realizable only if the network in question has a software defined architecture. A software defined network (SDN) has a centralized controller platform which continuously monitors the overall network condition and collects related information to manage flow control for intelligent networking. The ABS clients need not to make bitrate decisions but simply forward the observed network traffic and storage status to the SDN controller. Based on the SDN architecture, our numerical solution gives a resource allocation policy for ABS clients to achieve the mini-max QoE fairness in real-time. Both computer simulation and hardware implementation results are provided to verify the feasibility and efficiency of the proposed methods. We find that all three algorithms achieve the same QoE fairness (i.e., MMF).
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