%0 Report %1 seufert2014survey %A Seufert, Michael %A Egger, Sebastian %A Slanina, Martin %A Zinner, Thomas %A Hoßfeld, Tobias %A Tran-Gia, Phuoc %D 2014 %K "Quality Experience" HTTP adaptive dynamic myown of quality streaming variable video %N 490 %T A Survey on Quality of Experience of HTTP Adaptive Streaming %X Changing network conditions pose severe problems to video streaming in the Internet. HTTP adaptive streaming (HAS) is a technology employed by numerous video services which relieves these issues by adapting the video to the current network conditions. It enables service providers to improve resource utilization and Quality of Experience (QoE) by incorporating information from different layers in order to deliver and adapt a video in its best possible quality. Thereby, it allows to take into account end user device capabilities, available video quality levels, current network conditions, and current server load. For end users, the major benefits of HAS compared to classical HTTP video streaming are reduced interruptions of the video playback and higher bandwidth utilization, which both generally result in a higher QoE. Adaptation is possible by changing the frame rate, resolution, or quantization of the video, which can be done with various adaptation strategies and related client- and server-side actions. The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this article as fundament to derive the QoE influence factors which emerge as a result of adaptation. The main contribution is a comprehensive survey of QoE related works from human computer interaction and networking domains which are structured according to the QoE impact of video adaptation. To be more precise, subjective studies which cover QoE aspects of adaptation dimensions and strategies are revisited. As a result, QoE influence factors of HAS and corresponding QoE models are identified, but also open issues and conflicting results are discussed. Furthermore, the interaction between HAS and other clients, which is often ignored in the context of HAS, affects QoE influence factors and is consequently analyzed. This survey gives the reader an overview of the current state of the art and recent developments. At the same time it targets networking researchers who develop new solutions for HTTP video streaming or assess video streaming from a user centric point of view. Therefore, the article is a major step towards truly improving HAS.

@techreport{seufert2014survey, abstract = {Changing network conditions pose severe problems to video streaming in the Internet. HTTP adaptive streaming (HAS) is a technology employed by numerous video services which relieves these issues by adapting the video to the current network conditions. It enables service providers to improve resource utilization and Quality of Experience (QoE) by incorporating information from different layers in order to deliver and adapt a video in its best possible quality. Thereby, it allows to take into account end user device capabilities, available video quality levels, current network conditions, and current server load. For end users, the major benefits of HAS compared to classical HTTP video streaming are reduced interruptions of the video playback and higher bandwidth utilization, which both generally result in a higher QoE. Adaptation is possible by changing the frame rate, resolution, or quantization of the video, which can be done with various adaptation strategies and related client- and server-side actions. The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this article as fundament to derive the QoE influence factors which emerge as a result of adaptation. The main contribution is a comprehensive survey of QoE related works from human computer interaction and networking domains which are structured according to the QoE impact of video adaptation. To be more precise, subjective studies which cover QoE aspects of adaptation dimensions and strategies are revisited. As a result, QoE influence factors of HAS and corresponding QoE models are identified, but also open issues and conflicting results are discussed. Furthermore, the interaction between HAS and other clients, which is often ignored in the context of HAS, affects QoE influence factors and is consequently analyzed. This survey gives the reader an overview of the current state of the art and recent developments. At the same time it targets networking researchers who develop new solutions for HTTP video streaming or assess video streaming from a user centric point of view. Therefore, the article is a major step towards truly improving HAS. }, added-at = {2015-06-18T10:00:28.000+0200}, author = {Seufert, Michael and Egger, Sebastian and Slanina, Martin and Zinner, Thomas and Hoßfeld, Tobias and Tran-Gia, Phuoc}, biburl = {https://www.bibsonomy.org/bibtex/2732e3f6067c6850c37c4faef1983cf25/trcsuniwue}, interhash = {0cb337cd9e9edfe9f29fcf14fec53581}, intrahash = {732e3f6067c6850c37c4faef1983cf25}, keywords = {"Quality Experience" HTTP adaptive dynamic myown of quality streaming variable video}, month = {April}, number = 490, timestamp = {2015-06-18T10:00:28.000+0200}, title = {A Survey on Quality of Experience of HTTP Adaptive Streaming}, year = 2014 }