ABSTRACT
In this chapter, research about the assessment of video quality for gaming content will be provided. At first, a dataset that was used for the development of the ITU-T Rec. G.1072 will be presented. The dataset was created in a laboratory environment using the passive test paradigm described in Chap. 3. Next, some results of the collected video quality ratings will be illustrated. While QoE assessment studies traditionally make use of controlled laboratory environments, there are also other possibilities to conduct user studies without a laboratory environment. Especially during the COVID-19 pandemic, which prevented many researchers from performing lab studies, the concept of supervised and unsupervised remote studies got lots of attention. By using such a remote study design, two studies assessing video quality ratings of similar conditions as in the previously mentioned dataset were conducted. These two studies allow to address three research topics that will be the focus of the remainder of this chapter. At first, it will be investigated whether video quality ratings obtained using the remote study design are comparable to those collected in the lab environment. Second, a comparison between video quality ratings collected using a stimulus duration of 20 s instead of 30 s will be performed, which tries to answer whether it is enough to use a shorter stimulus duration as proposed in ITU-T Rec. P.809. Lastly, the differences between using a discrete 5-point ACR scale and the extended continuous 7-point scales will be investigated. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
In this chapter, research about the assessment of video quality for gaming content will be provided. At first, a dataset that was used for the development of the ITU-T Rec. G.1072 will be presented. The dataset was created in a laboratory environment using the passive test paradigm described in Chap. 3. Next, some results of the collected video quality ratings will be illustrated. While QoE assessment studies traditionally make use of controlled laboratory environments, there are also other possibilities to conduct user studies without a laboratory environment. Especially during the COVID-19 pandemic, which prevented many researchers from performing lab studies, the concept of supervised and unsupervised remote studies got lots of attention. By using such a remote study design, two studies assessing video quality ratings of similar conditions as in the previously mentioned dataset were conducted. These two studies allow to address three research topics that will be the focus of the remainder of this chapter. At first, it will be investigated whether video quality ratings obtained using the remote study design are comparable to those collected in the lab environment. Second, a comparison between video quality ratings collected using a stimulus duration of 20 s instead of 30 s will be performed, which tries to answer whether it is enough to use a shorter stimulus duration as proposed in ITU-T Rec. P.809. Lastly, the differences between using a discrete 5-point ACR scale and the extended continuous 7-point scales will be investigated. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Recently, the usage of 360-degree videos has prevailed in various sectors such as education, real estate, medical, entertainment and more. The development of the Virtual World “Metaverse” demanded a Virtual Reality (VR) environment with high immersion and a smooth user experience. However, various challenges are faced to provide real-time streaming due to the nature of high-resolution 360-degree videos such as high bandwidth requirement, high computing power and low delay tolerance. To overcome these challenges, streaming methods such as Dynamic Adaptive Streaming over HTTP (DASH), Tiling, Viewport-Adaptive and Machine Learning (ML) are discussed. Moreover, the superiorities of the development of 5G and 6G networks, Mobile Edge Computing (MEC) and Caching and the Information-Centric Network (ICN) approaches to optimize the 360-degree video streaming are elaborated. All of these methods strike to improve the Quality of Experience (QoE) and Quality of Service (QoS) of VR services. Next, the challenges faced in QoE modeling and the existing objective and subjective QoE assessment methods of 360-degree video are presented. Lastly, potential future research that utilizes and further improves the existing methods substantially is discussed. With the efforts of various research studies and industries and the gradual development of the network in recent years, a deep fake virtual world, “Metaverse” with high immersion and conducive for daily life working, learning and socializing are around the corner.
ABSTRACT
Official statistics indicate that internet users all around the world watch more videos and play more games during the COVID-19 pandemic than at any time [18]. This unprecedented, challenging situation demands solutions to accommodate rapid growth while maintaining and/or enhancing the video quality. This paper proposes SODA-Stream, an SDN-based optimization framework for enhancing Quality-of-Experience (QoE) in DASH streaming. The optimization framework max-imizes the number of concurrent streaming sessions that can be accommodated in a network and maximize streaming quality. The practical implementation of the framework utilizes the dynamic routing and bandwidth allocation enabled by Software Defined Networking (SDN). The evaluation results show that SODA-Stream significantly outperforms the conventional network routing and resource allocation algorithms, accepting 52% more sessions, 45% improvement in bandwidth allocation, and 70% reduction in bandwidth wastage, smoother playback, and better viewing experience. © 2022 IEEE.