The usage of HTTP Adaptive Streaming (HAS) technology by content providers is increasing rapidly. Having available the video content in multiple qualities, using HAS allows to adapt the quality of downloaded video to the current network conditions providing smooth video-playback. However, the time-varying video quality by itself introduces a new type of impairment. The quality adaptation can be done in different ways. In order to find the best adaptation strategy maximizing users perceptual quality it is necessary to investigate about the subjective perception of adaptation-related impairments. However, the novelties of these impairments and their comparably long time duration make most of the standardized assessment methodologies fall less suited for studying HAS degradation. Furthermore, in traditional testing methodologies, the quality of the video in audiovisual services is often evaluated separated and not in the presence of audio. Nevertheless, the requirement of jointly evaluating the audio and the video within a subjective test is a relatively under-explored research field. In this work, we address the research question of determining the appropriate assessment methodology to evaluate the sequences with time-varying quality due to the adaptation. This was done by studying the influence of different adaptation related parameters through two different subjective experiments using a methodology developed to evaluate long test sequences. In order to study the impact of audio presence on quality assessment by the test subjects, one of the experiments was done in the presence of audio stimuli. The experimental results were subsequently compared with another experiment using the standardized single stimulus Absolute Category Rating (ACR) methodology.
With the recent increased popularity and high usage of HTTP Adaptive Streaming (HAS) techniques, various studies have been carried out in this area which generally focused on the technical enhancement of HAS technology and applications. However, a lack of common HAS standard led to multiple proprietary approaches which have been developed by major Internet companies. In the emerging MPEG-DASH standard the packagings of the video content and HTTP syntax have been standardized; but all the details of the adaptation behavior are left to the client implementation. Nevertheless, to design an adaptation algorithm which optimizes the viewing experience of the enduser, the multimedia service providers need to know about the Quality of Experience (QoE) of different adaptation schemes. Taking this into account, the objective of this experiment was to study the QoE of a HAS-based video broadcast model. The experiment has been carried out through a subjective study of the end user response to various possible clients’ behavior for changing the video quality taking different QoE-influence factors into account. The experimental conclusions have made a good insight into the QoE of different adaptation schemes which can be exploited by HAS clients for designing the adaptation algorithms.
The streaming of 3D video contents is currently a reality to expand the user experience. However, because of the
variable bandwidth of the networks used to deliver multimedia content, a smooth and high-quality playback experience could not always be guaranteed. Using segments in multiple video qualities, HTTP adaptive streaming (HAS) of video content is a relevant advancement with respect to classic progressive download streaming. Mainly, it allows resolving these issues by offering significant advantages in terms of both user-perceived Quality of Experience (QoE) and resource utilization for content and network service providers. In this paper we discuss the impact of possible HAS client’s behavior while adapting to the network capacity on enduser. This has been done through an experiment of testing the end-user response to the quality variation during the adaptation procedure. The evaluation has been carried out through a subjective test of the end-user response to various possible clients’ behaviors for increasing, decreasing, and oscillation of quality in 3D video. In addition, some of the HAS typical impairments during the adaptation has been simulated and their effects on the end-user perception are assessed. The experimental conclusions have made good insight into the user’s response to different adaptation scenarios and visual impairments causing the visual discomfort that can be used to develop the adaptive streaming algorithm to improve the end-user experience.