  Quality of experience (QoE) assessment for adaptive video streaming plays a
significant role in advanced network management systems. It is especially
challenging in case of dynamic adaptive streaming schemes over HTTP (DASH)
which has increasingly complex characteristics including additional playback
issues. In this paper, we provide a brief overview of adaptive video streaming
quality assessment. Upon our review of related works, we analyze and compare
different variations of objective QoE assessment models with or without using
machine learning techniques for adaptive video streaming. Through the
performance analysis, we observe that hybrid models perform better than both
quality-of-service (QoS) driven QoE approaches and signal fidelity measurement.
Moreover, the machine learning-based model slightly outperforms the model
without using machine learning for the same setting. In addition, we find that
existing video streaming QoE assessment models still have limited performance,
which makes it difficult to be applied in practical communication systems.
Therefore, based on the success of deep learned feature representations for
traditional video quality prediction, we also apply the off-the-shelf deep
convolutional neural network (DCNN) to evaluate the perceptual quality of
streaming videos, where the spatio-temporal properties of streaming videos are
taken into consideration. Experiments demonstrate its superiority, which sheds
light on the future development of specifically designed deep learning
frameworks for adaptive video streaming quality assessment. We believe this
survey can serve as a guideline for QoE assessment of adaptive video streaming.
