Big Data-backed video distribution in the telecom cloud

Telecom operators are starting the deployment of Content Delivery Networks (CDN) to better control and manage video contents injected into the network. Cache nodes placed close to end users can manage contents and adapt them to users' devices, while reducing video traffic in the core. By adopting the standardized MPEG-DASH technique, video contents can be delivered over HTTP. Thus, HTTP servers can be used to serve contents, while packagers running as software can prepare live contents. This paves the way for virtualizing the CDN function. In this paper, a CDN manager is proposed to adapt the virtualized CDN function to current and future demand. A Big Data architecture, fulfilling the ETSI NFV guide lines, allows controlling virtualized components while collecting and pre-processing data. Optimization problems minimize CDN costs while ensuring the highest quality. Re-optimization is triggered based on threshold violations; data stream mining sketches transform collected into modeled data and statistical linear regression and machine learning techniques are proposed to produce estimation of future scenarios. Exhaustive simulation over a realistic scenario reveals remarkable costs reduction by dynamically reconfiguring the CDN.Peer ReviewedPostprint (author's final draft

A machine learning-based framework for preventing video freezes in HTTP adaptive streaming

HTTP Adaptive Streaming (HAS) represents the dominant technology to deliver videos over the Internet, due to its ability to adapt the video quality to the available bandwidth. Despite that, HAS clients can still suffer from freezes in the video playout, the main factor influencing users' Quality of Experience (QoE). To reduce video freezes, we propose a network-based framework, where a network controller prioritizes the delivery of particular video segments to prevent freezes at the clients. This framework is based on OpenFlow, a widely adopted protocol to implement the software-defined networking principle. The main element of the controller is a Machine Learning (ML) engine based on the random undersampling boosting algorithm and fuzzy logic, which can detect when a client is close to a freeze and drive the network prioritization to avoid it. This decision is based on measurements collected from the network nodes only, without any knowledge on the streamed videos or on the clients' characteristics. In this paper, we detail the design of the proposed ML-based framework and compare its performance with other benchmarking HAS solutions, under various video streaming scenarios. Particularly, we show through extensive experimentation that the proposed approach can reduce video freezes and freeze time with about 65% and 45% respectively, when compared to benchmarking algorithms. These results represent a major improvement for the QoE of the users watching multimedia content online

On the Bitrate Adaptation of Shared Media Experience Services

In Shared Media Experience Services (SMESs), a group of people is interested in streaming consumption in a synchronised way, like in the case of cloud gaming, live streaming, and interactive social applications. However, group synchronisation comes at the expense of other Quality of Experience (QoE) factors due to both the dynamic and diverse network conditions that each group member experiences. Someone might wonder if there is a way to keep a group synchronised while maintaining the highest possible QoE for each one of its members. In this work, at first we create a Quality Assessment Framework capable of evaluating different SMESs improvement approaches with respect to traditional metrics like media bitrate quality, playback disruption, and end user desynchronisation. Secondly, we focus on the bitrate adaptation for improving the QoE of SMESs, as an incrementally deployable end user triggered approach, and we formulate the problem in the context of Adaptive Real Time Dynamic Programming (ARTDP). Finally, we develop and apply a simple QoE aware bitrate adaptation mechanism that we compare against youtube live-streaming traces to find that it improves the youtube performance by more than 30%

Livenet: A low-latency video transport network for large-scale live streaming

Low-latency live streaming has imposed stringent latency requirements on video transport networks. In this paper we report on the design and operation of the Alibaba low-latency video transport network, LiveNet. LiveNet builds on a flat CDN overlay with a centralized controller for global optimization. As part of this, we present our design of the global routing computation and path assignment, as well as our fast data transmission architecture with fine-grained control of video frames. The performance results obtained from three years of operation demonstrate the effectiveness of LiveNet in improving CDN performance and QoE metrics. Compared with our prior state-of-The-Art hierarchical CDN deployment, LiveNet halves the CDN delay and ensures 98% of views do not experience stalls and that 95% can start playback within 1 second. We further report our experiences of running LiveNet over the last 3 years

Quality of experience-centric management of adaptive video streaming services : status and challenges

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

FlexStream: SDN-Based Framework for Programmable and Flexible Adaptive Video Streaming

With the tremendous increase in video traffic fueled by smartphones, tablets, 4G LTE networks, and other mobile devices and technologies, providing satisfactory services to end users in terms of playback quality and a fair share of network resources become challenging. As a result, an HTTP video streaming protocol was invented and widely adopted by most video providers today with the goal of maximizing the user’s quality of experience. However, despite the intensive efforts of major video providers such as YouTube and Netflix to improve their players, several studies as well as our measurements indicate that the players still suffer from several performance issues including instability and sub-optimality in the video bitrate, stalls in the playback, unfairness in sharing the available bandwidth, and inefficiency with regard to network utilization, considerably degrading the user’s QoE. These issues are frequently experienced when several players start competing over a common bottleneck. Interestingly, the root cause of these issues is the intermittent traffic pattern of the HTTP adaptive protocol that causes the players to over-estimate the available bandwidth and stream unsustainable video bitrates. In addition, the wireless network standards today do not allow the network to have a fine-grain control over individual devices which is necessary for providing resource usage coordination and global policy enforcement. We show that enabling such a network-side control would drive each device to fairly and efficiently utilize the network resources based on its current context, which would result in maximizing the overall viewing experience in the network and optimizing the bandwidth utilization. In this dissertation, we propose FlexStream, a flexible and programmable Software-Defined Network (SDN) based framework that solves all the adaptive streaming problems mentioned above. We develop FlexStream on top of the SDN-based framework that extends SDN functionality to mobile end devices, allowing for a fine-grained control and management of bandwidth based on real time context-awareness and specified policy. We demonstrate that FlexStream can be used to manage video delivery for a set of end devices over WiFi and cellular links and can effectively alleviate common problems such as player instability, playback stalls, large startup delay, and inappropriate bandwidth allocation. FlexStream offloads several tasks such as monitoring and policy enforcement to end-devices, while a network element (i.e., Global Controller), which has a global view of a network condition, is primarily employed to manage the resource allocation. This also alleviates the need for intrusive, large and costly traffic management solutions within the network, or modifications to servers that are not feasible in practice. We define an optimization method within the global controller for resource allocation to maximize video QoE considering context information, such as screen size and user priority. All features of FlexStream are implemented and validated on real mobile devices over real Wi-Fi and cellular networks. To the best of our knowledge, FlexStream is the first implementation of SDN-based control in a live cellular network that does not require any internal network support for SDN functionality

Improving Network Performance Through Endpoint Diagnosis And Multipath Communications

Components of networks, and by extension the internet can fail. It is, therefore, important to find the points of failure and resolve existing issues as quickly as possible. Resolution, however, takes time and its important to maintain high quality of service (QoS) for existing clients while it is in progress. In this work, our goal is to provide clients with means of avoiding failures if/when possible to maintain high QoS while enabling them to assist in the diagnosis process to speed up the time to recovery. Fixing failures relies on first detecting that there is one and then identifying where it occurred so as to be able to remedy it. We take a two-step approach in our solution. First, we identify the entity (Client, Server, Network) responsible for the failure. Next, if a failure is identified as network related additional algorithms are triggered to detect the device responsible. To achieve the first step, we revisit the question: how much can you infer about a failure using TCP statistics collected at one of the endpoints in a connection? Using an agent that captures TCP statistics at one of the end points we devise a classification algorithm that identifies the root cause of failures. Using insights derived from this classification algorithm we identify dominant TCP metrics that indicate where/why problems occur. If/when a failure is identified as a network related problem, the second step is triggered, where the algorithm uses additional information that is collected from ``failed\u27\u27 connections to identify the device which resulted in the failure. Failures are also disruptive to user\u27s performance. Resolution may take time. Therefore, it is important to be able to shield clients from their effects as much as possible. One option for avoiding problems resulting from failures is to rely on multiple paths (they are unlikely to go bad at the same time). The use of multiple paths involves both selecting paths (routing) and using them effectively. The second part of this thesis explores the efficacy of multipath communication in such situations. It is expected that multi-path communications have monetary implications for the ISP\u27s and content providers. Our solution, therefore, aims to minimize such costs to the content providers while significantly improving user performance