With the increasing popularity of multimedia-rich web portals, reducing latency has become more and more important. The current median web page load time is in the order of seconds, while research has shown that user waiting times must remain below two seconds to achieve optimal acceptance. In this paper, we analyzed a large dataset obtained from a major Belgian news provider, focusing on content popularity, user activity and user preference towards article news categories. Based on this analysis, we introduce the concept of personal delivery networks (PDNs), in which content is stored closer to the end user, at delivery caches in the edge of the core network or even in the access network. PDN nodes proactively prefetch and evict content on a per-user basis, opening opportunities for personalized low-latency delivery of multimedia-rich web applications. Initial results show that a PDN-based approach allows to significantly reduce the average latency.

HTTP Adaptive Streaming (HAS) is the de facto standard for video streaming services over the Internet. In HAS, each video is temporally segmented and stored in different qualities. The client selects the quality level for every video segment based on network conditions, allowing a smooth playback with the best possible Quality of Experience (QoE). Although results are promising, current solutions suffer from two problems. First, a low quality and large end-to-end latency are often observed in live streaming scenarios. Second, freezes in the video playout may occur in case of sudden drops of the available bandwidth. We reduced these issues using two complementary approaches. First, we reduced the live latency using the new HTTP/2 server push in combination with super-short segments. Second, we designed an OpenFlow-based network controller that prioritizes the delivery of particular segments to avoid freezes at the clients. The proof-of-concept shows the results obtained when two clients stream a video under varying network conditions. By monitoring the clients' behavior, it is possible to understand the gains brought by the proposed approaches. Particularly, we demonstrate how our solutions consistently reduce the live latency in high round-trip time networks and video freezes caused by network congestion. These results represent a major improvement for the QoE of the final users.

HTTP Adaptive Streaming (HAS) is the de facto standard for over-the-top video streaming. In HAS, video content is encoded at multiple quality levels and temporally divided into multiple segments. The client can select the quality level for every video segment, allowing smoother playback and a better Quality of Experience (QoE). Although results are promising, current solutions often suffer from high round-trip time (RTT) cycles in mobile networks. This is especially true for scalable video coding (SVC), where multiple requests are required to retrieve a single video segment. Meanwhile, the IETF has standardized the HTTP/2 protocol since February 2015, providing new features that allow a reduction of the page load time in Web browsing. In this paper, we propose a novel approach based on HTTP/2's server push feature to actively push the base layer of live, SVC-encoded content from server to client. This allows to eliminate one RTT cycle for every video segment, which has a significant impact on the user's QoE. Evaluating the proposed approach, we show that compared with HTTP/1.1, an improvement of 65.42% can be achieved for the average video quality in high-RTT networks. Compared to an AVC-based solution, the freeze frequency and duration are reduced by 54.55% and 53.06% respectively, while the loss in video quality is limited to 4.51%. Since playout freezes should be avoided at the cost of a lower video quality, we conclude that the proposed approach beneficially impacts the user's QoE.

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for video streaming services over the Internet. In HAS, each video is segmented and stored in different qualities. Rate adaptation heuristics, deployed at the client, allow the most appropriate quality level to be dynamically requested, based on the current network conditions. Current heuristics under-perform when sudden bandwidth drops occur, therefore leading to freezes in the video play-out, the main factor influencing users' Quality of Experience (QoE). In this article, we propose an Openflow-based framework capable of increasing clients' QoE by reducing video freezes. An Openflow-controller is in charge of introducing prioritized delivery of HAS segments, based on feedback collected from both the network nodes and the clients. To reduce the side-effects introduced by prioritization on the bandwidth estimation of the clients, we introduce a novel mechanism to inform the clients about the prioritization status of the downloaded segments without introducing overhead into the network. This information is then used to correct the estimated bandwidth in case of prioritized delivery. By evaluating this novel approach through emulation, under varying network conditions and in several multi-client scenarios, we show how the proposed approach can reduce freezes up to 75% compared to state-of-the-art heuristics.

HTTP adaptive streaming (HAS) has become a key video delivery technology for mobile and fixed networks. Internet service providers and CDN (Content Delivery Network) providers are interested in network-based monitoring the client's Quality of Experience (QoE) for HAS sessions. In our previous work, we designed a HAS QoE monitoring system based on the sequence of HTTP GET requests collected at the CDN nodes. The system relies on a technique called session reconstruction to retrieve the major QoE parameters without modification of the clients. However, session reconstruction is computationally intensive and requires manual configuration of reconstruction rules. To overcome the limitations of session reconstruction, this paper proposes a scalable machine learning (ML) based scheme that detects video freezes using a few high-level features extracted from the network-based monitoring data. We determine the discriminative features for session representation and assess five potential classifiers. We select the C4.5 decision tree as classifier because of its simplicity, scalability, accuracy, and explainability. To evaluate our solution, we use traces of Apple HTTP Live Streaming video sessions obtained from a number of operational CDN nodes and traces of Microsoft Smooth Streaming video sessions acquired in a controlled lab environment. Experimental results show that an accuracy of about 98%, 98%, and 90% can be obtained for the detection of a video freeze, a long video freeze, and multiple video freezes, respectively. Excluding log parsing, the computational cost of the proposed video-freeze detection is 33 times smaller than needed for session reconstruction.

HTTP Adaptive Streaming (HAS) is today the number one video technology for over-the-top video distribution. In HAS, video content is temporally divided into multiple segments and encoded at different quality levels. A client selects and retrieves per segment the most suited quality version to create a seamless playout. Despite the ability of HAS to deal with changing network conditions, HAS-based live streaming often suffers from freezes in the playout due to buffer under-run, low average quality, large camera-to-display delay, and large initial/channel-change delay. Recently, IETF has standardized HTTP/2, a new version of the HTTP protocol that provides new features for reducing the page load time in Web browsing. In this paper, we present ten novel HTTP/2-based methods to improve the quality of experience of HAS. Our main contribution is the design and evaluation of a push-based approach for live streaming in which super-short segments are pushed from server to client as soon as they become available. We show that with an RTT of 300 ms, this approach can reduce the average server-to-display delay by 90.1% and the average start-up delay by 40.1%.

Crosstalk is a major issue in modern digital subscriber line (DSL) systems such as ADSL and VDSL. Static spectrum management, which is the traditional way of ensuring spectral compatibility, employs spectral masks that can be overly conservative and lead to poor performance. This paper presents a centralized algorithm for optimal spectrum balancing in DSL. The algorithm uses the dual decomposition method to optimize spectra in an efficient and computationally tractable way. The algorithm shows significant performance gains over existing dynamics spectrum management (DSM) techniques, e.g., in one of the cases studied, the proposed centralized algorithm leads to a factor-of-four increase in data rate over the distributed DSM algorithm iterative waterfilling.

Dynamic spectrum management (DSM) is a new technique for multiuser power allocation and/or detection in digital subscriber line (DSL) networks. In DSM, transmit spectra are adapted based on the direct and crosstalk channels seen by the modems within a network. This allows binder bit rate performance maximisation. At the Alcatel Research and Innovation labs, we have recently developed a DSM testbed which allows the performance of DSM algorithms to be evaluated in practice. The paper describes the development of this DSM testbed. With this testbed we have evaluated the performance of a DSM algorithm known as iterative water-filling. It has been demonstrated that iterative water-filling can yield data-rate gains of up to 500% in the real world. Practical issues on the implementation of iterative water-filling are also discussed.

Crosstalk is the major source of performance degradation in next generation DSL systems such as VDSL. In downstream communications, transmitting modems are co-located at the central office. This allows crosstalk precompensation to be employed. In crosstalk precompensation, the transmitted signal is pre-distorted such that the pre-distortion destructively interferes with the crosstalk introduced by the channel. Existing crosstalk precompensation techniques either give poor performance or require modification of customer premises equipment (CPF). This is impractical since there are millions of legacy CPE modems already in use. We present a novel crosstalk precompensation technique, based on a diagonalization of the crosstalk channel matrix. This technique does not require modification of CPE. Furthermore, certain properties of the DSL channel ensure that this diagonalizing precompensator achieves near-optimal performance.

Crosstalk is a major issue in modern DSL systems such as ADSL and VDSL. Static spectrum management, the traditional way of ensuring spectral compatibility, employs spectral masks which can be overly conservative and lead to poor performance. In this paper we present a centralized algorithm for optimal spectrum management (OSM) in DSL. The algorithm uses a dual decomposition to solve the spectrum management problem in an efficient and computationally tractable way. The algorithm shows significant performance gains over existing DSM techniques, e.g. in a downstream ADSL scenario the centralized OSM algorithm can outperform a distributed DSM algorithm such as iterative waterfilling by up to 135%.

Qualifying a subscriber loop for xDSL transmission can be done in many ways. Amongst all ways, methods that don't require an expensive truck-roll to the customer are preferred. In this paper we provide a method for an intelligent automated interpretation of reflectometric 1-port measurements at the central office. First, we focus on how reflectograms can be characterised. Next, we build up intelligence in the system to deal with the uncertainty on the characteristics and to infer the topology by means of a belief network. Eventually, we combine in an artificial intelligent way, by means of a rule-based expert system all available knowledge and derive the basic loop properties i.e. the topology and the delays of the individual line sections.

In order to qualify a subscriber loop for xDSL transmission, the channel capacity has to be estimated, which depends on the transfer function of the network. A method is provided to estimate the transfer function of the subscriber loop only measuring the one-port scattering parameter at the central office. We consider three types of networks according to their topology: a single line, a homogeneous network with a bridged tap, and a cascade of two line sections. For each type of network a parametric model is derived of its one-port scattering parameter and transfer function based on the physical line model VUB0. The model for the scattering parameter is used to identify the network based on the corresponding measurements by means of a maximum-likelihood estimator. The estimated parameters are substituted in the transfer function model, which is needed for the capacity estimation. The proposed models and estimators are validated by measurements and simulations. For the measurements, which were performed with a network analyzer, three types of twisted-pair cables were used: British Telecom (BT), Deutsch Telekom (FT), and Belgacom.