The study focuses on the adaptation problem for HTTP low-delay live streaming over mobile networks. In this context, the client's small buffer could be easily underflown due to throughput variations. To maintain seamless streaming, we present a probabilistic approach to adaptively decide the bitrate for each video segment by taking into account the instant buffer level. The experimental results show that the proposed method can significantly reduce buffer underflows while providing high video bitrates.

360 videos have recently become a popular virtual reality content type. However, a good quality metric for 360 videos is still an open issue. In this work, our goal is to identify appropriate objective quality metrics for 360 video communications. Especially, fourteen objective quality measures at different processing phases are considered. Also, a subjective test is conducted in this study. The relationship between objective quality and subjective quality is investigated. It is found that most of the PSNR-related quality measures are well correlated with subjective quality. However, for evaluating video quality across different contents, a content-based quality metric is needed.

Video streaming over HTTP/2 is a new trend in multimedia delivery. Compared to the pull-based HTTP/1.1 protocol, the new HTTP/2 protocol's Server Push feature is very effective in reducing the overheads (e.g., in terms of energy, processing, bandwidth) for clients, servers, and network nodes. This paper presents an HTTP/2 push-based adaptation method for on-demand video streaming that reduces the number of requests and provides high video quality. In our method, for each client request, the server sends video segments continuously until it receives another client request with a new quality. Since a request is sent only if the client wants to update the video bitrate, our method can significantly reduce the request related overhead. For this context, a buffer based algorithm is proposed to provide high and smooth video quality while avoiding buffer underflows. Experiments show that the proposed method can provide a lower number of requests, higher average quality and better quality smoothness than existing methods.

HTTP Adaptive Streaming (HAS) has become a popular solution for media delivery over the mobile Internet. However, existing HAS systems are based on the pull-based HTTP/1.1 protocol, leading to high overheads (e.g., in terms of energy, processing, bandwidth) for clients, servers, as well as network nodes. The new HTTP/2 protocol provides a server push feature, which allows the client to receive more than one video segment for each request in order to reduce request-related overheads. In this study, we propose an adaptation method to leverage the push feature of HTTP/2. Our method takes into account not only the request-related overhead but also buffer stability and gradual transitions. The experimental results show that our proposed method performs well under strong throughput variations of mobile networks.

It is crucial to provide Internet videos with the best possible content value (or quality) to users. To adapt to network fluctuations, existing solutions provide various client-based heuristics to change video versions without considering the actual quality. In this work, we present for the first time the use of a quality model in making adaptation decisions to improve the overall quality. The proposed method also estimates the buffer level in the near future to prevent the client from buffer underflows. Experiment results show that the proposed method is able to provide high and consistent video quality under strongly fluctuating bandwidths.

In this letter, we propose a solution for managing multiple adaptive streaming clients running on different devices in a wireless home network. Our solution consists of two main aspects: a manager that determines bandwidth allocated for each client and a client-based throughput control mechanism that regulates the video traffic throughput of each client. The experimental results using a real test-bed show that our solution is able to effectively improve the quality for concurrent streaming clients.

Thanks to the ability to bring immersive experiences to users, Virtual Reality (VR) technologies have been gaining popularity in recent years. A key component in VR systems is omnidirectional content, which can provide 360-degree views of scenes. However, at a given time, only a portion of the full omnidirectional content, called viewport, is displayed corresponding to the user's current viewing direction. In this work, we first develop Weighted-Viewport PSNR (W-VPSNR), an objective quality metric for quality assessment of omnidirectional content. The proposed metric takes into account the foveation feature of the human visual system. Then, we build a subjective database consisting of 72 stimuli with spatial varying viewport quality. By using this database, an evaluation of the proposed metric and four conventional metrics is conducted. Experiment results show that the W-VPSNR metric well correlates with the mean opinion scores (MOS) and outperforms the conventional metrics. Also, it is found that the conventional metrics do not perform well for omnidirectional content.

360-degree video delivery in Virtual Reality is very challenging due to the fact that 360-degree videos require much higher bandwidth than conventional videos. To overcome this problem, viewport-adaptive streaming has been introduced. In this study, we propose a new adaptation method for tiling-based viewport-adaptive streaming of 360-degree videos. For content preparation, the Cubemap projection format is used, where faces or parts of a face are encoded as tiles. Also, the problem is formulated as an optimization problem, in which each visible tile is weighted based on how that tile overlaps with the viewport. To solve the problem, an approximation algorithm is proposed in this study. An evaluation of the proposed method and reference methods is carried out under different tiling schemes and bandwidths. Experiments show that the Cubemap format with tiling provides a lot of benefits in terms of storage, viewport quality across different viewing directions and bandwidths, and tolerance to prediction errors.

HTTP Adaptive Streaming (HAS) has become a popular solution for multimedia delivery nowadays. Because of throughput variations, video quality fluctuates during a streaming session. Therefore, a main challenge in HAS is how to evaluate the overall video quality of a session. In this paper, we explore the impacts of quality values and quality variations in HAS. We propose to use the histogram of segment quality values and the histogram of quality gradients in a session to model the overall video quality. Subjective test results show that the proposed model has very high prediction performance for different videos. Especially, the proposed model provides insights into the influence factors of the overall quality, thus leading to suggestions to improve the quality of streaming video.