Theodoros Karagkioules

Theodoros Karagkioules, Georgios S. Paschos, Nikolaos Liakopoulos et al.

In this paper, we propose a novel algorithm for video rate adaptation in HTTP Adaptive Streaming (HAS), based on online learning. The proposed algorithm, named Learn2Adapt (L2A), is shown to provide a robust rate adaptation strategy which, unlike most of the state-of-the-art techniques, does not require parameter tuning, channel model assumptions or application-specific adjustments. These properties make it very suitable for mobile users, who typically experience fast variations in channel characteristics. Simulations show that L2A improves on the overall Quality of Experience (QoE) and in particular the average streaming rate, a result obtained independently of the channel and application scenarios.

Dimitrios Tsilimantos, Theodoros Karagkioules, Stefan Valentin

Accurate cross-layer information is very useful to optimize mobile networks for specific applications. However, providing application-layer information to lower protocol layers has become very difficult due to the wide adoption of end-to-end encryption and due to the absence of cross-layer signaling standards. As an alternative, this paper presents a traffic profiling solution to passively estimate parameters of HTTP Adaptive Streaming (HAS) applications at the lower layers. By observing IP packet arrivals, our machine learning system identifies video flows and detects the state of an HAS client's play-back buffer in real time. Our experiments with YouTube's mobile client show that Random Forests achieve very high accuracy even with a strong variation of link quality. Since this high performance is achieved at IP level with a small, generic feature set, our approach requires no Deep Packet Inspection (DPI), comes at low complexity, and does not interfere with end-to-end encryption. Traffic profiling is, thus, a powerful new tool for monitoring and managing even encrypted HAS traffic in mobile networks.

Sami Mekki, Theodoros Karagkioules, Stefan Valentin

In mobile networks, users may lose coverage when entering a building due to the high signal attenuation at windows and walls. Under such conditions, services with minimum bit-rate requirements, such as video streaming, often show poor Quality-of-Experience (QoE). We will present a Bayesian detector that combines measurements from two Smartphone sensors to decide if a user is inside a building or not. Based on this coverage classification, we will propose an HTTP adaptive streaming (HAS) algorithm to increase playback stability at a high average bitrate. Measurements in a typical office building show high accuracy for the presented detector and superior QoE for the proposed HAS algorithm.

Dimitrios Tsilimantos, Theodoros Karagkioules, Amaya Nogales-Gómez et al.

This paper describes a novel system that provides key parameters of HTTP Adaptive Streaming (HAS) sessions to the lower layers of the protocol stack. A non-intrusive traffic profiling solution is proposed that observes packet flows at the transmit queue of base stations, edge-routers, or gateways. By analyzing IP flows in real time, the presented scheme identifies different phases of an HAS session and estimates important application-layer parameters, such as play-back buffer state and video encoding rate. The introduced estimators only use IP-layer information, do not require standardization and work even with traffic that is encrypted via Transport Layer Security (TLS). Experimental results for a popular video streaming service clearly verify the high accuracy of the proposed solution. Traffic profiling, thus, provides a valuable alternative to cross-layer signaling and Deep Packet Inspection (DPI) in order to perform efficient network optimization for video streaming.

Theodoros Karagkioules, Dimitrios Tsilimantos, Cyril Concolato et al.

HTTP Adaptive Streaming (HAS) techniques are now the dominant solution for video delivery in mobile networks. Over the past few years, several HAS algorithms have been introduced in order to improve user quality-of-experience (QoE) by bit-rate adaptation. Their difference is mainly the required input information, ranging from network characteristics to application-layer parameters such as the playback buffer. Interestingly, despite the recent outburst in scientific papers on the topic, a comprehensive comparative study of the main algorithm classes is still missing. In this paper we provide such comparison by evaluating the performance of the state-of-the-art HAS algorithms per class, based on data from field measurements. We provide a systematic study of the main QoE factors and the impact of the target buffer level. We conclude that this target buffer level is a critical classifier for the studied HAS algorithms. While buffer-based algorithms show superior QoE in most of the cases, their performance may differ at the low target buffer levels of live streaming services. Overall, we believe that our findings provide valuable insight for the design and choice of HAS algorithms according to networks conditions and service requirements.