Ran Dubin

Adaptation logic for HTTP dynamic adaptive streaming using geo-predictive crowdsourcing for mobile users

The increasing demand for video streaming services with a high Quality of Experience (QoE) has prompted considerable research on client-side adaptation logic approaches. However, most algorithms use the client’s previous download experience and do not use a crowd knowledge database generated by users of a professional service. We propose a new crowd algorithm that maximizes the QoE. We evaluate our algorithm against state-of-the-art algorithms on large, real-life, crowdsourcing datasets. There are six datasets, each of which contains samples of a single operator (T-Mobile, AT&T or Verizon) from a single road (I100 or I405). All measurements were from Android cellphones. The datasets were provided by WeFi LTD and are public for academic users. Our new algorithm outperforms all other methods in terms of QoE (eMOS).

A novel multicast adaptive logic for dynamic adaptive streaming over HTTP network

Video streaming is now responsible for the majority of Internet traffic and is expected to keep growing over the coming years. Dynamic Adaptive Streaming over HTTP (DASH) [1] is an ISO/IEC MPEG multi-quality layer streaming solution that is designed to enable interoperability between servers and clients of different vendors. In the DASH protocol, the client-side player is assumed to have Adaptation Logic (AL). The AL evaluates the various video representation segments available on the server and chooses the most suitable segments balancing between video quality and switching time. Note that dynamic adaptation is necessary due to the fact that the network bandwidth (e.g. cellular network) and the users buffer are not stable and have a high influence on re-buffering. However, to date, none of the research considers multicast conditions and therefore, there is no AL specifically designed to support multicast at the client side. In this paper, we present the Harmonic Mean Adaptive Logic (HMAL) which is a buffer sensitive adaptation logic that first calculates how many segments exist in the buffer and then estimates the channel bandwidth using the harmonic mean of the previous n samples. The HMAL is designed to support multicast networks by reducing the weights of hight quality segments in the bandwidth estimation and give more weight to lower quality segments. Compared to the multicast versions of well known ALs, the simulation results showed that HMAL has the best bandwidth estimation, the lowest number of re-buffering events, and the highest buffer efficiency.

Multicast adaptive logic for Dynamic Adaptive Streaming over HTTP network

Video streaming constitutes the vast majority of Internet traffic and the DASH protocol has become the de-facto standard in the industry of multimedia delivery. The multicast method for information distribution has the potential to dramatically reduce multimedia streaming traffic; however, to date, there is no effective Adaptive Logic (AL) designed to support multicast constraints at the client side. In this paper we present an adaptive logic that is designed specifically for multicast scenarios. A comparison of our Multicast Adaptive Logic (MAL) with leading ALs under multicast conditions demonstrates that MAL provides the best performance under multicast conditions and good performance under unicast conditions.

Video quality representation classification of Safari encrypted DASH streams

The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet Inspection (DPI). However, Google and certain content providers have started to encrypt their video services. As a result, operators often encounter difficulties in shaping their encrypted video traffic via DPI. This highlights the need for new traffic classification methods for encrypted HTTP adaptive video streaming to enable smart traffic shaping. These new methods will have to effectively estimate the quality representation layer and playout buffer. We present a new method and show for the first time that video quality representation classification for (YouTube) encrypted HTTP adaptive streaming is possible. We analyze the performance of this classification method with Safari over HTTPS. Based on a large number of offline and online traffic classification experiments, we demonstrate that it can independently classify, in real time, every video segment into one of the quality representation layers with 96.13% average accuracy.

Novel ad insertion technique for MPEG-DASH

Dynamic Adaptive Streaming over HTTP (DASH) is a new and promising streaming protocol, based on the Media Presentation Description (MPD) specification. With the increasing demand for Internet video streaming, methods for profiting from video services are gaining increased interest. In this paper, we propose a novel algorithm for server side video ad insertion over the DASH standard. The proposed method is compatible with DASH and does not require any modifications at the client side, such as dedicated players, or any modification in the MPD definitions. Furthermore, the algorithm enables the client to always receive the advertisements regardless of his player software. Our novel approach considers the MPD URLs as encrypted URLs for pointer mapping. This will allow for deciding in real time whether a specific URL will point to an ad or to the original video stream segment. Therefore, the solution enables us to consider VOD ad insertion as similar to live ad insertion. In a comparison between the DASH video streaming server with ad insertion, as define in the standard, and our ad insertion solution, the results showed that our solution provide a dynamic ad insertion system while only slightly increasing the CPU load. As far as we know, this is the first DASH server side ad insertion solution for the ISO Base Media File Format (MPEG-4 part 12) container.

I Know What You Saw Last Minute—Encrypted HTTP Adaptive Video Streaming Title Classification

Desktops can be exploited to violate privacy. There are two main types of attack scenarios: active and passive. We consider the passive scenario where the adversary does not interact actively with the device, but is able to eavesdrop on the network traffic of the device from the network side. In the near future, most Internet traffic will be encrypted and thus passive attacks are challenging. Previous research has shown that information can be extracted from encrypted multimedia streams. This includes video title classification of non HTTP adaptive streams. This paper presents algorithms for encrypted HTTP adaptive video streaming title classification. We show that an external attacker can identify the video title from video HTTP adaptive streams sites, such as YouTube. To the best of our knowledge, this is the first work that shows this. We provide a large data set of 15000 YouTube video streams of 2100 popular video titles that was collected under real-world network conditions. We present several machine learning algorithms for the task and run a thorough set of experiments, which shows that our classification accuracy is higher than 95%. We also show that our algorithms are able to classify video titles that are not in the training set as unknown and some of the algorithms are also able to eliminate false prediction of video titles and instead report unknown. Finally, we evaluate our algorithm robustness to delays and packet losses at test time and show that our solution is robust to these changes.

Video complexity hybrid traffic shaping for HTTP Adaptive Streaming

The increasing demand for video content and the fast adoption of HTTP Adaptive Streaming (HAS) has led to the need for sophisticated streaming optimization solutions. One of the main drawbacks of HAS is that the user is responsible for deciding which video quality to request without taking into account the server load, the number of users, fairness and more. Therefore, traffic shaping server, which takes these factors into account is needed. In this paper we present a HAS traffic shaping algorithm that in one hand tries to maximize user experience by providing the quality which is the closest to the one that the user requested while in other hand takes into account the server constrains. Simulation results show that the proposed solution effectively serves up to a 28% more users when the network is congested, while the users experienced an average bit-rate decreased up to 12% and the average PSNR decrease was 0.26 dB.

A fair server adaptation algorithm for HTTP adaptive streaming using video complexity

The increasing popularity of online video content and adaptive video streaming services, especially those based on HTTP Adaptive Streaming (HAS) highlights the need for streaming optimization solutions. From a server perspective, the main drawback of HAS is that the user selects the quality of the next video segment without taking the server constraints into account. These constraints include the number of users simultaneously being served and the server’s congestion. Here, we present the Fair Server Adaptation (FSA) algorithm, which is designed to maximize user Quality of Experience (QoE) by tackling the server’s bottleneck problem. The algorithm provides the quality representation that is closest to the user’s request, subject to the server’s constraints. Simulation results show that compared to standard Dynamic Adaptive Streaming over HTTP (DASH) server, FSA increased the number of served users and decreased both the number of rebuffering events and the average rebuffering event duration. Furthermore, the average number of unserved users decreased to almost zero and Jain’s fairness index rose. It is clear that these changes increase users’ QoE.

Analyzing HTTPS encrypted traffic to identify user's operating system, browser and application

Desktops and laptops can be maliciously exploited to violate privacy. There are two main types of attack scenarios: active and passive. In this paper, we consider the passive scenario where the adversary does not interact actively with the device, but he is able to eavesdrop on the network traffic of the device from the network side. Most of the internet traffic is encrypted and thus passive attacks are challenging. In this paper, we show that an external attacker can identify the operating system, browser and application of HTTP encrypted traffic (HTTPS). To the best of our knowledge, this is the first work that shows this. We provide a large data set of more than 20000 examples for this task. Additionally, we suggest new features for this task.We run a through a set of experiments, which shows that our classification accuracy is 96.06%.