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.
In this paper, we present a Mobile Edge Computing (MEC) scheme for enabling network edge-assisted video adaptation based on MPEG-DASH (Dynamic Adaptive Streaming over HTTP). In contrast to the traditional over-the-top (OTT) adaptation performed by DASH clients, the MEC server at the mobile network edge can capture radio access network (RAN) conditions through its intrinsic Radio Network Information Service (RNIS) function, and use the knowledge to provide guidance to clients so that they can perform more intelligent video adaptation. In order to support such MEC-assisted DASH video adaptation, the MEC server needs to locally cache the most popular content segments at the qualities that can be supported by the current network throughput. Towards this end, we introduce a two-dimensional user Quality-of-Experience (QoE)-driven algorithm for making caching replacement decisions based on both content context (e.g., segment popularity) and network context (e.g., RAN downlink throughput). We conducted experiments by deploying a prototype MEC server at a real LTE-A based network testbed. The results show that our QoE-driven algorithm is able to achieve significant improvement on user QoE over 2 benchmark schemes. With the rapid development of Mobile Internet, online Video-on-Demand (VoD) services, primarily 4K video, grow tremendously with the key performance indicators of lower latency, higher bandwidth, and higher bitrate. However, due to the long-distance between the user equipment (UE) and Internet Service Provider, Quality-of-Service (QoS) in terms of low playback delay and high transmission rate cannot be guaranteed. Therefore, Mobile Edge Computing (MEC), at the edge of the cellular network, is highly recommended with the benefits of lower uncertainty and end-to-end latency. The UE can enjoy better customized services with more appropriate bitrates as a result. In this paper, we propose a practical framework of MEC-enabled cellular network with radio network-aware edge cache and a radio network-aware cache updating algorithm. The framework uses Dynamic Adaptive Streaming over HTTP (DASH) and Radio Network Information Service (RNIS) is leveraged to accelerate multi-media services. Under this framework, RNIS collects information and delivers it to the MEC server based on the context. A testbed based on a real 4G Long Term Evolution (LTE) Base Station is developed carrying out experiments under this framework. Compared to traditional networks, the result shows that our approach maintains a smooth high quality of experience (QoE). Internet video streaming applications have been demanding more bandwidth and higher video quality, especially with the advent of virtual reality and augmented reality appli-cations. While adaptive strea ming protocols like MPEG-DASH (dynamic adaptive streaming over HTTP) allows video quality to be flexibly adapted, e.g., degraded when mobile network condition deteriorates, this is not an option if the application itself requires guaranteed 4K quality at all time. On the other hand, conventional end-to-end transmission control protocol (TCP) has been struggling in supporting 4K video delivery across long-distance Internet paths containing both fixed and mobile network segments with heterogeneous characteristics. In this paper, we present a novel and practically feasible system architecture named MVP (mobile edge virtualization with adaptive prefetching), which enables content providers to embed their content intelligence as a virtual network function into the mobile network operator's infrastructure edge. Based on this architecture, we present a context-aware adaptive video prefetching scheme in order to achieve quality of experience (QoE)-assured 4K video on demand (VoD) delivery across the global Internet. Through experiments based on a real LTE-A network infrastructure, we demonstrate that our proposed scheme is able to achieve QoE-assured 4K VoD streaming, especially when the video source is located remotely in the public Internet, in which case none of the state-of-the-art solutions is able to support such an objective at global Internet scale.  Dynamic adaptive streaming over Hypertext Transfer Protocol (HTTP) is an advanced technology in video streaming to deal with the uncertainty of network states. However, this technology has one drawback as the network states frequently and continuously change. The quality of a video streaming fluctuates along with the network changes, and it might reduce the quality of service. In recent years, many researchers have proposed several adaptive streaming algorithms to reduce such changes. However, these algorithms only consider the current state of a network. Thus, these algorithms might result in inaccurate estimates of a video quality in the near term. Therefore, in this paper, we propose a method using fuzzy logic and a mathematics moving average technique, in order to reduce mobile video quality fluctuation in Dynamic Adaptive Streaming over HTTP (DASH). First, we calculate the moving average of the bandwidth and buffer values for a given period. On the basis of differences between real and average values, we propose a fuzzy logic system to deduce the value of the video quality representation for the next request. In addition, we use the entropy rate of a bandwidth measurement sequence to measure the predictable stabilization of our method. The experiment results show that our proposed method reduces video quality fluctuation as well as improves 40 of bandwidth utilization compared to existing methods.