David Cecil Robinson

Subjective video quality assessment of HTTP adaptive streaming technologies

HTTP adaptive steaming (HAS) is becoming ubiquitous as a reliable method of delivering video content over the open Internet to a variety of devices from personal computers (PCs), to tablets, game consoles, and smartphones. HAS is able to adapt to both the available bandwidth and the display requirements by trading-off video quality. This paper describes two experiments to test end user subjective response to this varying quality. First, we tested three commercially available HAS products in our viewing room. This allowed us to control the introduction of network impairments and to record the mean opinion score (MOS). In a second experiment, we generated clips with impairments typical of HAS. These were downloaded and commented on by a group of young people. This provided insight into the response of users to different types of visual impairments and hence what steps can be taken to improve the end user experience.

Mobile video optimization at the base station: Adaptive guaranteed bit rate for HTTP adaptive streaming

Video is expected to be the dominant application by traffic volume over mobile networks in the near future. Mobile network operators are deploying video optimization techniques to enhance the user experience and network utilization for video delivery. Video optimization is typically “out-of-network” and includes techniques such as transcoding, transrating, time shifting and pacing that are implemented outside of mobile radio access and core networks. However, such techniques cannot easily exploit information about real time cell congestion and radio conditions for the video terminals because it is difficult to obtain such information from the radio access network. We propose a novel “in-network” video optimization technique, named Adaptive Guaranteed Bit Rate (AGBR), for HTTP-based Adaptive Streaming (HAS) video. This optimization technique is implemented at the base station and can thus exploit knowledge of the radio and congestion conditions. With only limited knowledge of the video stream properties or content, AGBR works by adjusting the throughput delivered to the different HAS clients that in turn adjust the video quality they request. The optimization algorithm maximizes aggregate quality across multiple video flows served by the base station without starving data clients, thereby improving the overall quality of experience. We demonstrate through extensive analytical modeling and simulations that AGBR can adapt to changing network conditions to support more video sessions at an acceptable quality than alternative algorithms, while enforcing fairness among all users competing for resources within a sector.