Rate-Quality or Energy-Quality Pareto Fronts for Adaptive Video Streaming?

Redirection and protocol techniques are key components of the infrastructure for Content Delivery Networks (CDNs) that aid in the delivery of Multimedia Internet services to end-consumers. Redirection methods are used to route the user’s request to the nearest edge server, minimizing distance, improving delivery times, and lowering latency. Protocol mechanisms, such as HTTP Live Streaming (HLS), Dynamic Adaptive Streaming over HTTP (DASH), and Real-Time Messaging Protocol (RTMP), are used to deliver adaptive video streaming. These protocols are designed to transfer the adaptive streaming and provide high-quality video playback. They also allow the system to adjust the video quality based on the network conditions of the Quality of Service (QoS). Inadequate transmission protocols and poorly designed redirection algorithms are two major challenges that might degrade Cloud–CDN performance. These challenges lead to excessive latency, poor quality of service, and significant packet loss that have potential influences on the user experience. In this paper, firstly, three protocols are proposed by preparing a case study on selecting the optimal protocol for replicating adaptive video streaming content. Secondly, a redirection algorithm based on the Modified Cuckoo Search Algorithm (MCSA) is proposed to provide an accurate redirecting process of selected edge servers to end-users. Test results indicate that, when hybrid FASP/HTTP protocols were chosen (from original server to replicate server and to end-users), the delivery of adaptive video streaming segments was fast with lower latency. The average estimated time needed for replicating video content based on FASP is 25% better than that needed for File catalyst and Signiant protocols. Therefore, the Cuckoo search method presents more efficient results for selecting the optimal edge server for 100 servers, which is 0.296 s, compared to the conventional algorithm, which was 13 s.

Video streaming currently dominates global Internet traffic. Live streaming broadcasts events in real-time, with very different characteristics compared to video-on-demand (VoD), being more sensitive to variations in delay, jitter, and packet loss. The use of adaptive streaming techniques over HTTP is massively deployed on the Internet, adapting the video quality to instantaneous condition of the network. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular adaptive streaming technology. In DASH, the client probes the network quality and adjusts the quality of requested video segment according to the bandwidth fluctuations. Therefore, DASH is an over-the-top application using unmanaged networks to distribute content in the best possible quality. In order to maintain a seamless playback, VoD applications commonly use a large reception buffer. However, in live streaming, the use of large buffers is not allowed because of the induced delay. Active Queue Management (AQM) arises as an alternative to control the congestion in router’s queue, pressing the traffic sources to reduce their transmission rate when it detects incipient congestion. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming. Furthermore, we propose a new AQM algorithm to improve the user-perceived video quality. The results show that the proposed method achieves better performance than competing AQM algorithms and improves the video quality in terms of average peak signal-to-noise ratio while keeping the fairness among concurrent flows.

A brief survey on adaptive video streaming quality assessment

HTTP adaptive streaming accounts for a large part of mobile Internet traffic. With the developing cellular communication technologies, the standard dynamic adaptive streaming over HTTP technique allows mobile terminals to adaptively configure the transmission rate of video streaming applications and achieve high quality of experiences (QoE) with many adaptive bitrate algorithms. However, existing schemes either neglect to consider radio access network (RAN) side conditions or only consider simple RAN information optimization schemes. In this letter, we propose a QoE-oriented adaptive video streaming scheme based on a dueling deep Q-learning network. The proposed scheme improves the QoE by jointly considering the physical layer transmission bandwidth and the higher layer buffer status. Through the numerical and prototyping results, we show that our proposed scheme outperforms the existing schemes, with the average QoE improvements of 12.6% to 28.8%.

Adaptive video streaming improves users' quality of experience (QoE), while using the network efficiently. In the last few years, adaptive video streaming has seen widespread adoption and has attracted significant research effort. We study a dynamic system of random arrivals and departures for different classes of users using the adaptive streaming industry standard DASH (Dynamic Adaptive Streaming over HTTP). Using a Markov chain based analysis, we compute the user QoE metrics: probability of starvation, prefetching delay, average video quality and switching rate. We validate our model by simulations, which show a very close match. Our study of the playout buffer is based on client adaptation scheme, which makes efficient use of the network while improving users' QoE. We prove that for buffer-based variants, the average video bit-rate matches the average channel rate. Hence, we would see quality switches whenever the average channel rate does not match the available video bit rates. We give a sufficient condition for setting the playout buffer threshold to ensure that quality switches only between adjacent quality levels.

Internet video streaming is a hot topic in multimedia systems. A large variety of devices (computers, mobile phones, TVs, etc.) are connected to the Internet via wired or wireless networks and are capable of receiving and decoding HD video content. To enable new services like HD video streaming (e.g., online video rental), the Internet’s infrastructure was enhanced. But the Internet is still a best-effort network, which does not implement quality-of-service or admission control, resulting in time-varying bandwidth and packet delay, packet loss and network congestion. Because video streaming accounts for a considerable amount of the Internet’s traffic, video streaming needs additionally to be congestion-aware, to avoid a congestion collapse of the Internet. The Transmission Control Protocol (TCP) can adapt to changing network conditions and is currently the de facto standard protocol for congestion-aware and reliable data transmission in the Internet. This fact gave TCP-based video streaming a huge momentum. Consequently, this thesis investigates TCP-based adaptive video streaming for the Internet. The main goal is to provide a solution for congestion-aware video streaming, while still being able to achieve a reasonable performance in error-prone networks. To complement existing work on congestion-aware adaptive streaming, this thesis makes six contributions. (1) The baseline performance of TCP-based adaptive streaming is identified by means of an evaluation of different adaptive streaming approaches. The results represent a reference for further investigations. (2) An investigation on the influence of TCP’s behavior in presence of packet loss on the video streaming performance. (3) To overcome the shortcomings of TCP-based video streaming (single TCP connections fail to deliver a good performance in case of packet loss), a new approach to video streaming based on multiple request-response streams was introduced. The novelty of this system is that it is able to make use of multiple HTTP-based request-response streams while still providing TCP-friendliness. (4) A performance model of the HTTP-based request-response streams was developed, to estimate the influence of the system parameters and the network characteristics on the throughput performance. (5) A comprehensive evaluation of the HTTP-based request-response streams under diverse network conditions was conducted, to validate the model’s estimations. Additionally, the TCP-friendliness was evaluated, showing that request-response streaming systems can be configured to achieve TCP-friendliness. (6) A cellular network with high bandwidth fluctuations and RTTs was used to investigate the performance of the request-response streaming system in a mobile video streaming scenario. The results indicate that the streaming system can make good use of the available bandwidth, while the number of quality switches is kept low. While aggregating multiple TCP connections to improve the TCP streaming performance is quite common, usually the improvement comes at the cost of high deployment effort. By placing the streaming logic at the client, request-response streams can avoid this complexity. Additionally, this client-driven approach responds faster to changing network conditions and enables easy recovery from connection stalls or aborts, because the control loop is at the client. To improve the network efficiency and the scalability in terms of number of clients served, HTTP-based request-response streams can utilize HTTP proxies and caches.

Most commercial video streaming systems rely on Content Distribution Networks (CDNs) to distribute video content. HTTP adaptive streaming has been recently adopted by major video streaming providers and is now considered the standard technique used with CDN-based streaming systems. Despite the success of these systems, cost-effective scalability continues to be of concern in their design and deployment. To address this, recent work has proposed the use of hybrid CDN and Peer-to-peer (P2P) live streaming systems. The design of these systems aims to combine the scalability of P2P systems and the desirable performance properties of CDN-based systems. However, the use of adaptive streaming, has not been explored extensively in such hybrid systems. Designing and operating an adaptive hybrid streaming system is very challenging. Two design decisions are very critical in the operation of any such system. The first one is the bitrate adaptation strategy which specifies how different bitrates are assigned to different users while maximizing user satisfaction. The second is defining the operational guidelines for switching the system between the CDN and the P2P modes while efficiently utilizing the available resources. In this paper we present a model and analysis of a hybrid CDN-P2P adaptive live streaming system with the objective of answering these two design questions. We first present a stochastic fluid model to the hybrid streaming system with a single video bitrate and we obtain theoretical results to guide the system operation as described above. We then extend the analysis to the adaptive streaming case with multiple video bitrates. We model adaptive streaming as a linear optimization problem to obtain the best bitrate adaptation strategy. We validate our analysis using simulations. Our conclusion is that adaptive hybrid streaming can significantly improve the ability of the system to satisfy more users with higher video bitrates over CDN-based systems.

Joint source and sending rate modeling in adaptive video streaming

인터넷을 이용하는 사용자의 관점이 호스트 중심에서 콘텐트 중심으로 변화하면서 콘텐트 중심 네트워크 (Content Centric Network, 이하 CCN)라는 새로운 패러다임이 소개되었다. 한편, 최근 비디오 스트리밍에 대한 수요가 급증하고 있으며 더 높은 사용자의 만족도를 위한 적응형 스트리밍이 소개되면서 많은 연구가 진행 중에 있다. 따라서 CCN에서도 사용자의 수요에 따라 적응형 스트리밍을 고려할 필요성이 있다. 하지만 CCN에서 기존의 네트워크 구조에서와 동일한 방식으로 적응형 비디오 스트리밍 서비스를 할 경우 CCN 라우터 내 캐시를 (CS) 충분히 활용하지 못한다는 한계점이 있으며 또한 단말의 달라지는 요구 사항을 캐시 활용에 반영할 수 없는 문제점도 있다. 따라서 본 논문에서는 정규표현식을 활용한 콘텐트 네이밍 방식을 적용하여 기존 적응형 스트리밍 비트레이트 선택 알고리즘의 캐시활용도를 높이면서도 CCN의 기본 프로토콜에 적합한 프레임워크를 제시하고, 단말의 상태에 따라 동적인 표현식 기술 전략 및 선택 알고리즘을 통하여 비디오 스트리밍 품질을 개선하고자 한다. Content Centric Network (CCN) has been introduced as a new paradigm due to a shift of users's perspective of using Internet from host-centric to content-centric. On the other hand, a demand for video streaming has been increasing. Thus, Adaptive streaming has been introduced and researched for achieving higher user's satisfaction. If an architecture of Internet is replaced with CCN architecture, it is necessary to consider adaptive video streaming in CCN according to the demand of users. However, if the same rate decision algorithm used in Internet is deployed in CCN, there are a limitation of utilizing content store (CS) in CCN router and a problem of reflecting dynamic requirements. Therefore, this paper presents a framework adequate to CCN protocol and cache utilization, adapting content naming method of exploiting regular expression to the rate decision algorithm of the existing adaptive streaming. In addition, it also improves the quality of video streaming and verifies the performance through dynamic expression strategies and selection algorithm of the strategies.

Under complex network conditions, adaptive video streaming requires additional state information for optimal quality selection. In this paper, we present the applicability of reinforcement learning techniques on NDN adaptive streaming. Both buffer-based and throughput-based adaptation are studied and observed their characteristics. The Q-learning algorithm is used to learn state-action values. Based on a greedy policy, the simulation results demonstrate that RL agents tend to choose the best possible bitrate which consequently reduces the quality fluctuation in adaptive streaming.

This paper presents a new Scalable Video Codec (SVC)-based hybrid adaptive video streaming scheme, named HAVS, for mobile devices in wireless environments. The proposed approach takes two existing video streaming technologies, viz., progressive download and adaptive streaming, and switches them in a hybrid manner. To this end, HAVS employs the H.264/SVC encoding scheme, where each video chunk is encoded into one base layer and several enhancement layers. Since clients request the base layer every time a video is streamed, HAVS performs progressive download for the base layer and adaptive streaming for the enhancement layers. Through wireless test-bed experiments, it is demonstrated that the proposed scheme can be easily implemented on mobile devices without any server-side modification. This scheme effectively prevents video freeze thereby providing better quality video streaming than the existing non-hybrid streaming technologies.

This chapter discusses the different transport approaches for adaptive video streaming media, and how they influence the Quality of Experience (QoE). These approaches are based solely on the HTTP protocol, and are specially designed for video transportation over the Internet to support the wide range of devices and maximize end user's perceived quality. The leading groups and companies, e.g. Microsoft, Apple, Adobe and MPEG/3GPP have introduced their own standard approaches to facilitate the on-demand or live adaptive video streaming transport over HTTP. The main goals of adaptive video streaming are to improve and optimize user's QoE by changing the video quality according to network parameters, end user's device properties and other characteristics. There are five main quality metrics of video streaming that affect the user engagement during video watching, and influence user's QoE. The adaptive video streaming approaches use Transmission Control Protocol (TCP) as a transport protocol.

Adaptive rate video streaming on a novel Internet architecture, called Named Data Networking (NDN), appears to be a promising technology. However, given the source of content on NDN is unknown to the consumer, rate adaptation becomes inefficient. Moreover, the traffic at some nodes holding popular contents still can be congested. In this paper, we utilize the Internet of Things (IoT) device for assisting content delivery at an NDN router that has low bandwidth. The location of IoT devices installation is determined by a metric calculated from link bandwidth, delay, and utilization. We performed the experiments using DASH-NDN-JS on the setup network. The results demonstrate that our proposal could support NDN router to deliver video content to additional consumers efficiently. Thus, the network scalability can be improved with a reasonable cost.

Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming (HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users’ QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users’ QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users’ QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server- and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years.

To guarantee Quality of Experience (QoE) for video streaming services in a future Internet architecture, Content Centric Network (CCN), Dynamic Adaptive Streaming via HTTP (DASH) technology is used to deliver the proper video content according to the network situation. However, CCN enables a host-to-content communication model and has a universal caching design, which seriously decreases the performance of DASH over CCN. In this paper, we propose a hop-by-hop adaptive video streaming scheme (HAVS-CCN) to improve the performance of adaptive video streaming in CCN. HAVS-CCN is simple and applicable to be deployed on DASH over CCN. It directly adjusts video quality and solves network congestion at the bottleneck of transmission path when DASH inaccurately estimates the network throughout. Our scheme optimizes the hop-by-hop content transmission, which achieves video quality adaption and data packet flow control simultaneously. Simulation results, on small-scale networks and large-scale networks, reveal that DASH with HAVS-CCN scheme outperforms the original DASH over CCN, in terms of video playback quality and average delay.