Rafael Huysegems

SVC-based HTTP adaptive streaming

HTTP adaptive streaming (HAS) is rapidly evolving into a key video delivery technology, supported by implementations from Microsoft, Apple, and Adobe, and actively pursued by standardization organizations. Using segments in multiple video qualities, distributed via an already available Hypertext Transfer Protocol (HTTP) delivery infrastructure, a HAS client is able to seamlessly adapt to the available bandwidth in the network. However, existing HAS solutions have a number of disadvantages such as the additional storage and bandwidth requirements, a large playout buffer to absorb network impairments, and a non-optimal quality selection under fluctuating network conditions. In this paper, we investigate the opportunity of combining HAS with scalable video coding. We show that this combination creates possibilities to reduce the client buffer, which implies improvements for live and interactive video, and reduces storage requirements, increases the cache hit-ratio for supporting content delivery network (CDN) nodes, and demonstrates more robust behavior in the HAS client, ultimately improving the quality of experience (QoE) for the viewer.

HTTP/2-Based Methods to Improve the Live Experience of Adaptive Streaming

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%.

Session reconstruction for HTTP adaptive streaming: laying the foundation for network-based QoE monitoring

HTTP Adaptive Streaming (HAS) is rapidly becoming a key video delivery technology for fixed and mobile networks. However, today there is no solution that allows network operators or CDN providers to perform network-based QoE monitoring for HAS sessions. We present a HAS QoE monitoring system, based on data collected in the network, without monitoring information from the client. To retrieve the major QoE parameters such as average quality, quality variation, rebuffering events and interactivity delay, we propose a technique called session reconstruction. We define a number of iterative steps and developed algorithms that can be used to perform HAS session reconstruction. Finally, we present the results of a working prototype for the reconstruction and monitoring of Microsoft Smooth Streaming HAS sessions that is capable of dealing with intermediate caching and user interactivity. We describe the main observations when using the platform to analyze more than a hundred HAS sessions.

HTTP/2-Based Adaptive Streaming of HEVC Video Over 4G/LTE Networks

In HTTP Adaptive Streaming, video content is temporally divided into multiple segments, each encoded at several quality levels. The client can adapt the requested video quality to network changes, generally resulting in a smoother playback. Unfortunately, live streaming solutions still often suffer from playout freezes and a large end-to-end delay. By reducing the segment duration, the client can use a smaller temporal buffer and respond even faster to network changes. However, since segments are requested subsequently, this approach is susceptible to high round-trip times. In this letter, we discuss the merits of an HTTP/2 push-based approach. We present the details of a measurement study on the available bandwidth in real 4G/LTE networks, and analyze the induced bit-rate overhead for HEVC-encoded video segments with a sub-second duration. Through an extensive evaluation with the generated video content, we show that the proposed approach results in a higher video quality (+7.5%) and a lower freeze time (-50.4%), and allows to reduce the live delay compared with traditional solutions over HTTP/1.1.

Scalable network-based video-freeze detection for HTTP adaptive streaming

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 clients 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.

Enablers for non-linear video distribution

In the last decade, video distribution has evolved from analog broadcast to fully digital video. Today, users have the freedom to decide what to watch and when to watch it, using Internet video services. Recommendation and profiling systems are there to assist the user in creating a personalized content sequence. One step further, the content itself is personalized and enabled for interactive playout. Linear content such as daily news is analyzed, segmented, filtered, and reassembled per viewer based on his profile, preferences, and available time. The transport is typically performed in a unicast fashion from a central server, which places a high demand on the network since it prevents reuse. In this paper we introduce the paradigm of late assembly. Personalized and interactive videos are generated based on semantic segments and user profiles which are combined on intermediate nodes near the end user. Together with deadline scheduling and caching, an efficient transport is realized. An architecture and a prototype implementation of this system are presented.

An HTTP/2 Push-Based Approach for Low-Latency Live Streaming with Super-Short Segments

Over the last years, streaming of multimedia content has become more prominent than ever. To meet increasing user requirements, the concept of HTTP Adaptive Streaming (HAS) has recently been introduced. In HAS, video content is temporally divided into multiple segments, each encoded at several quality levels. A rate adaptation heuristic selects the quality level for every segment, allowing the client to take into account the observed available bandwidth and the buffer filling level when deciding the most appropriate quality level for every new video segment. Despite the ability of HAS to deal with changing network conditions, a low average quality and a large camera-to-display delay are often observed in live streaming scenarios. In the meantime, the HTTP/2 protocol was standardized in February 2015, providing new features which target a reduction of the page loading time in web browsing. In this paper, we propose a novel push-based approach for HAS, in which HTTP/2’s push feature is used to actively push segments from server to client. Using this approach with video segments with a sub-second duration, referred to as super-short segments, it is possible to reduce the startup time and end-to-end delay in HAS live streaming. Evaluation of the proposed approach, through emulation of a multi-client scenario with highly variable bandwidth and latency, shows that the startup time can be reduced with 31.2% compared to traditional solutions over HTTP/1.1 in mobile, high-latency networks. Furthermore, the end-to-end delay in live streaming scenarios can be reduced with 4 s, while providing the content at similar video quality.

An HTTP/2 push-based approach for SVC adaptive streaming

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/2s 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 users 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 users QoE.

Towards intelligent scheduling of multimedia content in future access networks

The popularity of streaming multimedia services has greatly increased in recent years. Telco- and cable-providers have started offering a plethora of multimedia services in the access and aggregation network, including video on demand, interactive digital television, and time-shifted TV. However, these services introduce additional challenges, such as stringent time constraints, and high bandwidth requirements. To overcome these problems, we explore the advantages of delivering such multimedia content using deadline-aware scheduling and caching algorithms. These algorithms decide when to send and store which content. This enables the network to optimize bandwidth consumption and satisfy deadline constraints. The designed algorithm was evaluated and compared to classical deadline-unaware delivery protocols. This allows us to study the efficiency of the new algorithm, and identify the scenarios in which deadline-aware scheduling improves delivery of multimedia content.

Network-based video freeze detection and prediction in HTTP adaptive streaming

Given the popularity of HTTP adaptive streaming (HAS) technology for media delivery over mobile and fixed networks, the clients Quality of Experience (QoE) for HAS video sessions is of particular interest for network providers and Content Delivery Network (CDN) operators. Despite that, network providers are not able to directly obtain QoE-relevant metrics such as video freezes, initial buffering time, and the frequency of quality switches from the client. This paper proposes a scalable machine learning (ML) based scheme that offline detects and online predicts video freezes using a few features extracted from the network-based monitoring data, i.e., a sequence of HTTP GET requests sent from the video client. We determine the discriminative features for detecting video freezes based on multi-scale windows using the criterion of information gain (IG). Four traditional classifiers are investigated and the C4.5 decision tree is selected because of its simplicity, scalability, accuracy, and interpretability. Our approach for session-based offline freeze detection is evaluated on the Apple HTTP Live Streaming video sessions obtained from a number of operational CDN nodes and on the traces of Microsoft Smooth Streaming video sessions acquired in a controlled lab environment. Experimental results show that, even with the disturbance of user interactivity, an accuracy of about 91% can be obtained for the detection of a video freeze, a long video freeze, and multiple video freezes. The experiments for the online freeze prediction show that more than 30% of the video freezes can be foreseen one segment in advance.