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Abstract. In this study, we propose a bandwidth-aware scaling mechanism for rate adaptive video streaming. This mechanism involves estimation of the capacity of the network dynamically by measuring bottleneck bandwidth and available bandwidth values. By taking the available bandwidth as an upper limit, the sender adjusts its output rate accordingly. While increasing the quality of the video, using a bandwidth estimator instead of probing prevents the congestion generated by the streaming application itself. The results of the bandwidth-aware algorithm are compared with that of a similar algorithm with no bandwidth-aware scaling and the improvement is demonstrated with measurements taken over WAN.

Bitrate adaptation algorithms have received considerable attention recently. In order to evaluate these algorithms objectively, multiple DASH datasets have been proposed. However, only few of them are compatible to SVC-based adaptation algorithms. Apart from the dataset, to fully implement and evaluate an adaptation algorithm, many time-consuming steps are required such as MPD parser design, adaptation logic design and network environment setup. In this paper, a dash simulator which assesses the performance of SVC-based adaptation algorithms without the requirement of any additional implementation steps is proposed. Also, an SVC dataset that includes both CBR and VBR encoded videos is designed. Demonstration is performed as evaluation of an SVC-based adaptation algorithm under several throughput scenarios using the designed dataset. Results show that the proposed system considerably reduces time requirement compared to real-time assessment. Dataset, throughput generation tool and simulator are all publicly available so that the researchers can test their implementation and compare with the results presented in this paper.

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