Video streaming currently accounts for the majority of Internet traffic. One factor that enables video streaming is HTTP Adaptive Streaming (HAS), that allows the users to stream video using a bit rate that closely matches the available bandwidth from the server to the client. MPEG Dynamic Adaptive Streaming over HTTP (DASH) is a widely used standard, that allows the clients to select the resolution to download based on their own estimations. The algorithm for determining the next segment in a DASH stream is not partof the standard, but it is an important factor in the resulting playback quality. Nowadays vehicles are increasingly equipped with mobile communication devices, and in-vehicle multimedia entertainment systems. In this paper, we evaluate the performance of various DASH adaptation algorithms over a vehicular network. We present detailed simulation results highlighting the advantages and disadvantages of various adaptation algorithms in delivering video content to vehicular users, and we show how the different adaptation algorithms perform in terms of throughput, playback interruption time, and number of interruptions.
Recent surveys @cite_4 @cite_3 give a good overview of the bit rate adaptation algorithms for DASH based content delivery.
@cite_4 @cite_3
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In recent years, quality of experience (QoE) has been investigated and proved to have both influential factors on user's visual quality and perceptual quality, while the perceptual quality means user's requirement on personalized content should be acquired in optimized quality. That's to say, those segments holding user interested content such as highlights need to be allocated more network resource in a resource-limited streaming scenario. However, all the existing HTTP-based adaptive methods only focus the content-agnostic bitrate adaptation according to limited network resources or energy resource, since they ignored user perceived semantics on some important segments, which suffered less quality on the important segments than on those ordinary ones, so as to hurt the overall QoE. In this paper, we have proposed a new semantic-aware adaptation scheme for MPEG-DASH services, which decides how to preserve bandwidth and buffering time depending on content descriptors for the perceived important content to users. Further, a semantic-aware probe and adaptation (SMA-PANDA) algorithm has been implemented in a DASH client to compare with conventional bitrate adaptions. Preliminary results show that SMA-PANDA achieves better QoE and flexibility on streaming user's interested content on MPEG-DASH platform, and it also aggressively helps user interested content compete more resource to deliver high quality presentation. With companies such as Netflix and YouTube accounting for more than 50 of the peak download traffic on North American fixed networks in 2015, video streaming represents a significant source of Internet traffic. Multimedia delivery over the Internet has evolved rapidly over the past few years. The last decade has seen video streaming transitioning from User Datagram Protocol to Transmission Control Protocol-based technologies. Dynamic adaptive streaming over HTTP (DASH) has recently emerged as a standard for Internet video streaming. A range of rate adaptation mechanisms are proposed for DASH systems in order to deliver video quality that matches the throughput of dynamic network conditions for a richer user experience. This survey paper looks at emerging research into the application of client-side, server-side, and in-network rate adaptation techniques to support DASH-based content delivery. We provide context and motivation for the application of these techniques and review significant works in the literature from the past decade. These works are categorized according to the feedback signals used and the end-node that performs or assists with the adaptation. We also provide a review of several notable video traffic measurement and characterization studies and outline open research questions in the field. In this paper, we provide some insight and background into the Dynamic Adaptive Streaming over HTTP (DASH) specifications as available from 3GPP and in draft version also from MPEG. Specifically, the 3GPP version provides a normative description of a Media Presentation, the formats of a Segment, and the delivery protocol. In addition, it adds an informative description on how a DASH Client may use the provided information to establish a streaming service for the user. The solution supports different service types (e.g., On-Demand, Live, Time-Shift Viewing), different features (e.g., adaptive bitrate switching, multiple language support, ad insertion, trick modes, DRM) and different deployment options. Design principles and examples are provided. Dynamic adaptive streaming over HTTP (DASH) has recently been widely deployed in the Internet. It, however, does not impose any adaptation logic for selecting the quality of video fragments requested by clients. In this paper, we propose a novel Markov decision-based rate adaptation scheme for DASH aiming to maximize the quality of user experience under time-varying channel conditions. To this end, our proposed method takes into account those key factors that make a critical impact on visual quality, including video playback quality, video rate switching frequency and amplitude, buffer overflow underflow, and buffer occupancy. Besides, to reduce computational complexity, we propose a low-complexity sub-optimal greedy algorithm which is suitable for real-time video streaming. Our experiments in network test-bed and real-world Internet all demonstrate the good performance of the proposed method in both objective and subjective visual quality.