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Abstract
HTTP adaptive streaming (HAS) has become a dominated media streaming paradigm in today’s Internet, which enriches the user’s experience by matching the video quality with the dynamic network conditions. A range of HAS mechanisms have been proposed to enhance the Quality of Experience (QoE). However, existing mechanisms ignore the environmental impact in the QoE evaluation of mobile users, while the popularity of mobile video allows users to watch videos in diversified scenarios. In this paper, we propose an environment-aware HAS scheme that fully concentrates on the different criteria for evaluating video QoE under different environments. Using the advantage of the sensors in mobile phones, the scheme constructs and validates a video QoE model based on environment perception and then designs a model-driven, environment-aware HAS rate adaptation algorithm. We also evaluate the scheme with an environment-aware DASH (Dynamic Adaptive Streaming over HTTP) player in real mobile environments. Compared to the benchmark HAS mechanism, the experimental results demonstrate that our scheme can provide appropriate differentiated rate adaptation for different environments, resulting in a higher QoE.
Highlights
Nowadays, with the development of mobile Internet technology and multimedia applications, video streaming is becoming one of the most dominant applications in the mobile Internet
It can be clearly seen that the environment-aware BOLA algorithm is more aggressive at the requested bit rate level
This paper proposes an environment-aware HTTP adaptive streaming (HAS) scheme for the differentiated Quality of Experience (QoE) evaluation criteria in different environments
Summary
With the development of mobile Internet technology and multimedia applications, video streaming is becoming one of the most dominant applications in the mobile Internet. Some important QoE evaluating metrics of HAS have been well studied, such as initial delay, stallings, the bit rate switching frequency by video adaptation strategy, and video quality with different resolutions or quantizations. In this paper, we propose an environment-aware QoE model and the corresponding model-driven HAS rate adaptation algorithm, which have been implemented by an environment-aware. The scheme carefully studies how the different QoE evaluation metrics enhance the QoE of HAS, and tries to provide a generalized environment-aware QoE model for HAS that extends the domain of the traditional QoE modeling methodology. The proposed HAS rate adaptation algorithm takes the environment-aware QoE model into account and provides a synergistic solution.
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