Evaluation of HTTP/DASH Adaptation Algorithms on Vehicular Networks

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

Provider-Controlled Bandwidth Management for HTTP-based Video Delivery

Over the past few years, a revolution in video delivery technology has taken place as mobile viewers and over-the-top (OTT) distribution paradigms have significantly changed the landscape of video delivery services. For decades, high quality video was only available in the home via linear television or physical media. Though Web-based services brought video to desktop and laptop computers, the dominance of proprietary delivery protocols and codecs inhibited research efforts. The recent emergence of HTTP adaptive streaming protocols has prompted a re-evaluation of legacy video delivery paradigms and introduced new questions as to the scalability and manageability of OTT video delivery. This dissertation addresses the question of how to enable for content and network service providers the ability to monitor and manage large numbers of HTTP adaptive streaming clients in an OTT environment. Our early work focused on demonstrating the viability of server-side pacing schemes to produce an HTTP-based streaming server. We also investigated the ability of client-side pacing schemes to work with both commodity HTTP servers and our HTTP streaming server. Continuing our client-side pacing research, we developed our own client-side data proxy architecture which was implemented on a variety of mobile devices and operating systems. We used the portable client architecture as a platform for investigating different rate adaptation schemes and algorithms. We then concentrated on evaluating the network impact of multiple adaptive bitrate clients competing for limited network resources, and developing schemes for enforcing fair access to network resources. The main contribution of this dissertation is the definition of segment-level client and network techniques for enforcing class of service (CoS) differentiation between OTT HTTP adaptive streaming clients. We developed a segment-level network proxy architecture which works transparently with adaptive bitrate clients through the use of segment replacement. We also defined a segment-level rate adaptation algorithm which uses download aborts to enforce CoS differentiation across distributed independent clients. The segment-level abstraction more accurately models application-network interactions and highlights the difference between segment-level and packet-level time scales. Our segment-level CoS enforcement techniques provide a foundation for creating scalable managed OTT video delivery services

Optimized Adaptive Streaming Representations based on System Dynamics

Adaptive streaming addresses the increasing and heterogenous demand of multimedia content over the Internet by offering several encoded versions for each video sequence. Each version (or representation) has a different resolution and bit rate, aimed at a specific set of users, like TV or mobile phone clients. While most existing works on adaptive streaming deal with effective playout-control strategies at the client side, we take in this paper a providers' perspective and propose solutions to improve user satisfaction by optimizing the encoding rates of the video sequences. We formulate an integer linear program that maximizes users' average satisfaction, taking into account the network dynamics, the video content information, and the user population characteristics. The solution of the optimization is a set of encoding parameters that permit to create different streams to robustly satisfy users' requests over time. We simulate multiple adaptive streaming sessions characterized by realistic network connections models, where the proposed solution outperforms commonly used vendor recommendations, in terms of user satisfaction but also in terms of fairness and outage probability. The simulation results further show that video content information as well as network constraints and users' statistics play a crucial role in selecting proper encoding parameters to provide fairness a mong users and to reduce network resource usage. We finally propose a few practical guidelines that can be used to choose the encoding parameters based on the user base characteristics, the network capacity and the type of video content

Subjective quality assessment of longer duration video sequences delivered over HTTP adaptive streaming to tablet devices

HTTP adaptive streaming facilitates video streaming to mobile devices connected through heterogeneous networks without the need for a dedicated streaming infrastructure. By splitting different encoded versions of the same video into small segments, clients can continuously decide which segments to download based on available network resources and device characteristics. These encoded versions can, for example, differ in terms of bitrate and spatial or temporal resolution. However, as a result of dynamically selecting video segments, perceived video quality can fluctuate during playback which will impact end-users' quality of experience. Subjective studies have already been conducted to assess the influence of video delivery using HTTP Adaptive Streaming to mobile devices. Nevertheless, existing studies are limited to the evaluation of short video sequences in controlled environments. Research has already shown that video duration and assessment environment influence quality perception. Therefore, in this article, we go beyond the traditional ways for subjective quality evaluation by conducting novel experiments on tablet devices in more ecologically valid testing environments using longer duration video sequences. As such, we want to mimic realistic viewing behavior as much as possible. Our results show that both video content and the range of quality switches significantly influence end-users' rating behavior. In general, quality level switches are only perceived in high motion sequences or in case switching occurs between high and low quality video segments. Moreover, we also found that video stallings should be avoided during playback at all times

Evaluation of H.265 Video Delivery Based on Dynamic Adaptive Streaming over HTTP (DASH) Using Mobile Devices

MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) is a standard for HTTP streaming that allows interoperability between servers and clients from different vendors which is able to adjust the speed of video delivery to changing bandwidth. Today there is the latest H.265 video coding known as the High Efficiency Video Coding (HEVC). This research implemented the delivery of the H.265 video through the method of Dynamic Adaptive Streaming over HTTP (DASH) using mobile devices. The test was conducted by sending videos over 3G networks with GSM length video segment consists of 2 seconds, 5 seconds, 10 seconds and 20 seconds. The video quality provided in this study consisted of 5 video levels with different bit rates. From the test results, the submitted videos using a smaller video segment generated greater throughput and acquired more video quality with a higher bit rate than the video to a larger segment. Video delivery with segment of 2 seconds acquired video quality with the highest level of 83%, meanwhile the video delivery segment of 20 seconds obtained video quality with the highest level of 33%

Energy-aware QoE and backhaul traffic optimization in green edge adaptive mobile video streaming

Collaborative caching and processing at the network edges through mobile edge computing (MEC) helps to improve the quality of experience (QoE) of mobile clients and alleviate significant traffic on backhaul network. Due to the challenges posed by current grid powered MEC systems, the integration of time-varying renewable energy into the MEC known as green MEC (GMEC) is a viable emerging solution. In this paper, we investigate the enabling of GMEC on joint optimization of QoE of the mobile clients and backhaul traffic in particularly dynamic adaptive video streaming over HTTP (DASH) scenarios. Due to intractability, we design a greedy-based algorithm with self-tuning parameterization mechanism to solve the formulated problem. Simulation results reveal that GMEC-enabled DASH system indeed helps not only to decrease grid power consumption but also significantly reduce backhaul traffic and improve average video bitrate of the clients. We also find out a threshold on the capacity of energy storage of edge servers after which the average video bitrate and backhaul traffic reaches a stable point. Our results can be used as some guidelines for mobile network operators (MNOs) to judge the effectiveness of GMEC for adaptive video streaming in next generation of mobile networks

Audiovisual Quality of Live Music Streaming over Mobile Networks using MPEG-DASH

The MPEG-DASH protocol has been rapidly adopted by most major network content providers and enables clients to make informed decisions in the context of HTTP streaming, based on network and device conditions using the available media representations. A review of the literature on adaptive streaming over mobile shows that most emphasis has been on adapting the video quality whereas this work examines the trade-off between video and audio quality. In particular, subjective tests were undertaken for live music streaming over emulated mobile networks with MPEG-DASH. A group of audio/video sequences was designed to emulate varying bandwidth arising from network congestion, with varying trade-off between audio and video bit rates. Absolute Category Rating was used to evaluate the relative impact of both audio and video quality in the overall Quality of Experience (QoE). One key finding from the statistical analysis of Mean Opinion Scores (MOS) results using Analysis of Variance indicates that providing reduced audio quality has a much lower impact on QoE than reducing video quality at similar total bandwidth situations. This paper also describes an objective model for audiovisual quality estimation that combines the outcomes from audio and video metrics into a joint parametric model. The correlation between predicted and subjective MOS was computed using several outcomes (Pearson and Spearman correlation coefficients, Root Mean Square Error (RMSE) and epsilon-insensitive RMSE). The obtained results indicate that the proposed approach is a viable solution for objective audiovisual quality assessment in the context of live music streaming over mobile network.info:eu-repo/semantics/acceptedVersio