Robust QoE-Driven DASH Over OFDMA Networks

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Abstract

In this paper, the problem of effective and robust delivery of Dynamic Adaptive Streaming over HTTP (DASH) videos over an orthogonal frequency-division multiplexing access (OFDMA) network is studied. Motivated by a measurement study, we propose to explore the request interval and robust rate prediction for DASH over OFDMA. We first formulate an offline cross-layer optimization problem based on a novel quality of experience (QoE) model. Then the online reformulation is derived and proved to be asymptotically optimal. After analyzing the structure of the online problem, we propose a decomposition approach to obtain a user equipment (UE) rate adaptation problem and a BS resource allocation problem. We introduce stochastic model predictive control (SMPC) to achieve high robustness on video rate adaption and consider the request interval for more efficient resource allocation. Extensive simulations show that the proposed scheme can achieve a better QoE performance compared with other variations and a benchmark algorithm, which is mainly due to its lower rebuffering ratio and more stable bitrate choices.

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