Conclusions & Future Work

In this paper, we first focus on how to optimize segment selections in HTTP adaptive streaming. In HAS and DASH, bitrate disparity between advertised bitrates and real bitrates violates the spirit of HAS that segments are selected based on estimated throughput and corresponding advertised bitrates. Streaming content of inconsistent quality causes buffer oscillations and also brings down reliability of buffer-based algorithms. Therefore, we provide accurate bitrate information of each segment by Subset elements in MPD to make user select correct segments across different encoding representations. As a consequence, the client rarely selects segments that make the network unsustainable when throughput estimation model works well. Even the client has to choose higher bitrates for certain quality of experience in some video encoding, the impact to buffer can be easily and clearly quantized with this method. On the other hand, the bandwidth competition on network bottleneck in HAS has been deeply investigated and many solutions have been proposed as well. In this paper, we propose an algorithm to detect oscillations in a detection window. The algorithm sets a ceil rate for the sequential requests and cut the ceil rate off when buffer has drained below a certain threshold. With real bitrate information in the first part, we can assure that clients will not request segments over the ceil rate. Our evaluation shows the minimum unfairness and inefficiency appear when both players run the algorithm.

In future work, we have to collect different types of bandwidth fluctuations and oscillation patterns based on connected networks and client locations. These will mature our algorithm to automatically adapt parameters in throughput estimation model and thresholds of test condition in the detection window.

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