An important component for viewport-adaptive streaming of 360° videos is viewport prediction. Increasing viewport prediction horizon enables the client to prefetch more chunks into the playback buffer. Having longer buffer results in less rebuffering under fluctuating network conditions. We analyzed the recorded viewport traces of viewers who watched various 360° videos. We propose a clustering-based viewport prediction method that incorporates viewport pattern information from previous video streaming sessions. For several videos, specifically those with well-defined region of interest, the proposed approach increases the viewport prediction horizon and/or prediction accuracy.

In this paper, we propose a taxonomy for 360° videos that categorizes videos based on moving objects and camera motion. We gathered and produced 28 videos based on the taxonomy, and recorded viewport traces from 60 participants watching the videos. In addition to the viewport traces, we provide the viewers' feedback on their experience watching the videos, and we also analyze viewport patterns on each category.

In recent years, there has been growing popularity of Virtual Reality (VR), enabled by technologies like 360° video streaming. Streaming 360° video is extremely challenging due to high bandwidth and low latency requirements. Some VR solutions employ adaptive 360° video streaming which tries to reduce bandwidth consumption by only streaming high resolution video for user's Field of View (FoV). FoV is the part of the video which is being viewed by the user at any given time. Although FoV-adaptive 360° video streaming has been helpful in reducing bandwidth requirements, streaming 360° video from distant content servers is still challenging due to network latency. Caching popular content close to the end users not only decreases network latency, but also alleviates network bandwidth demands by reducing the number of future requests that have to be sent all the way to remote content servers. In this paper, we propose a novel caching policy based on users' FoV, called FoV-aware caching policy. In FoV-aware caching policy, we learn a probabilistic model of common-FoV for each 360° video based on previous users' viewing histories to improve caching performance. Through experiments with real users' head movement dataset, we show that our proposed approach improves cache hit ratio compared to Least Frequently Used (LFU) and Least Recently Used (LRU) caching policies by at least 40% and 17%, respectively.

HTTP Adaptive Streaming (HAS) is the widely adopted solution for video streaming over the Internet. When network throughput is highly variable, designing an optimal HAS solution that maximizes Quality of Experience (QoE) becomes challenging. Each chunk should be prefetched at highest possible quality while rebufferings and quality switches are minimized. Scalable Video Coding (SVC), with its layered encoding of video, provides more flexibility for HAS clients. It can reduce the occurrence of rebufferings under variable network conditions. However, SVC introduces at least 10% overhead on video bitrate per layer and increases the number of HTTP requests to fetch video chunks. So streaming SVC video at high qualities is more expensive. We propose a solutions that employs both SVC and non-SVC video to improve user's QoE while avoiding the increased bandwidth overhead and HTTP signaling of SVC. Our experiments using real-world bandwidth traces show that this method improves QoE compared to the state-of-the-art adaptation methods under various network conditions.

Applications are typically offered a single, generic option for end-to-end transport, which most commonly consists of the particular flavor of TCP that runs in the server host. This arrangement does not suit well the diversity of the requirements that individual applications pose on network metrics like throughput and delay. Link-Coupled TCP (LCTCP) is a new transport solution that leverages the architectural trends of 5G networks to enable accurate satisfaction of the unique requirements of each application. LCTCP first isolates the 5G access link as the only possible network bottleneck for the application flow, then establishes a lightweight signaling channel between the link buffer and the application server to convey critical information for flexible control of the data source. LCTCP can be deployed in the network without modification of the TCP clients. We use a Linux prototype to demonstrate its feasibility and effectiveness.

Virtual reality and 360-degree video streaming are growing rapidly, yet, streaming high-quality 360-degree video is still challenging due to high bandwidth requirements. Existing solutions reduce bandwidth consumption by streaming high-quality video only for the user's viewport. However, adding the spatial domain (viewport) to the video adaptation space prevents the existing solutions from buffering future video chunks for a duration longer than the interval that user's viewport is predictable. This makes playback more prone to video freezes due to rebuffering, which severely degrades the user's Quality of Experience especially under challenging network conditions. We propose a new method that alleviates the restrictions on buffer duration by utilizing scalable video coding. Our method significantly reduces the occurrence of rebuffering on links with varying bandwidth without compromising playback quality or bandwidth efficiency compared to the existing solutions. We demonstrate the efficiency of our proposed method using experimental results with real world cellular network bandwidth traces.

Virtual reality and 360-degree video streaming are growing rapidly; however, streaming 360-degree video is very challenging due to high bandwidth requirements. To address this problem, the video quality is adjusted according to the user viewport prediction. High quality video is only streamed for the user viewport, reducing the overall bandwidth consumption. Existing solutions use shallow buffers limited by the accuracy of viewport prediction. Therefore, playback is prone to video freezes which are very destructive for the Quality of Experience (QoE). We propose using layered encoding for 360-degree video to improve QoE by reducing the probability of video freezes and the latency of response to the user head movements. Moreover, this scheme reduces the storage requirements significantly and improves in-network cache performance.