Traffic Profiling for Mobile Video Streaming

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.Comment: 7 pages, 11 figures. Accepted for publication in the proceedings of IEEE ICC'1

The Long Tail of Web Video

Web Video continues to gain importance not only in many areas of computer science but in society in general. With the growth in numbers, both of videos, viewers, and views, there arise several technical challenges. In order to address them effectively, the properties of Web Video in general need to be known. There is however comparatively little analysis of these properties. In this paper, we present insights gained from the analysis of a data set containing the meta data of over 100 million videos from YouTube. We were able to confirm common wisdom about the relationship between video duration and user engagement and show the extreme long tail of the distribution of video views overall. Such data can be beneficial in making informed decisions regarding strategies for large scale video storage, delivery, processing and retrieval

Mapping the ASEAN YouTube Uploaders

YouTube can now be categorized as mainstream media. It can be seen as a disruptive force in business and society, particularly concerning young people. There have been several recent studies about YouTube, providing essential insights on YouTube videos, viewers, social behavior, video traffic, and recommendation systems. However, research about YouTube uploaders has not been done much, especially YouTube uploaders from ASEAN countries. Using a combination of web content mining and content analysis, this paper reviews 600 YouTube uploaders using the data of Top 100 favorite YouTube uploaders in six ASEAN countries (Indonesia, Singapore, Malaysia, Thailand, Vietnam, and the Philippines), which are retrieved from NoxInfluencer. The study aims to provide a wider picture of YouTube uploaders' characteristics from six ASEAN countries. This study also provides useful information about how to retrieve web documents using Google Web Scrapper automatically. The study results found that the entertainment category dominated the top 100 positions of the NoxInfluencer version. In almost every country analyzed, channels related to news and politics are less attractive to YouTube users. For YouTube uploaders, YouTube can be a potential revenue source through advertising or in collaboration with specific brands. Through the analysis, we discovered that engagement is the critical factor in generating income in the form of likes, dislikes, and comments

A Data-Driven Traffic Steering Algorithm for Optimizing User Experience in Multi-Tier LTE Networks

Multi-tier cellular networks are a cost-effective solution for capacity enhancement in urban scenarios. In these networks, effective mobility strategies are required to assign users to the most adequate layer. In this paper, a data-driven self-tuning algorithm for traffic steering is proposed to improve the overall Quality of Experience (QoE) in multi-carrier Long Term Evolution (LTE) networks. Traffic steering is achieved by changing Reference Signal Received Quality (RSRQ)-based inter-frequency handover margins. Unlike classical approaches considering cell-aggregated counters to drive the tuning process, the proposed algorithm relies on a novel indicator, derived from connection traces, showing the impact of handovers on user QoE. Method assessment is carried out in a dynamic system-level simulator implementing a real multicarrier LTE scenario. Results show that the proposed algorithm significantly improves QoE figures obtained with classical load balancing techniques.Spanish Ministry of Economy and Competitiveness under Grant TEC2015-69982-R, in part by the Spanish Ministry of Education, Culture and Sports under FPU Grant FPU17/04286, and in part by the Horizon 2020 Project ONE5G under Grant ICT-76080

Modeling and Dimensioning of a Virtualized MME for 5G Mobile Networks

Network function virtualization is considered one of the key technologies for developing future mobile networks. In this paper, we propose a theoretical framework to evaluate the performance of a Long-Term Evolution (LTE) virtualized mobility management entity (vMME) hosted in a data center. This theoretical framework consists of 1) a queuing network to model the vMME in a data center and 2) analytic expressions to estimate the overall mean system delay and the signaling workload to be processed by the vMME. We validate our mathematical model by simulation. One direct use of the proposed model is vMME dimensioning, i.e., to compute the number of vMME processing instances to provide a target system delay given the number of users in the system. Additionally, the paper includes a scalability analysis of the system. In our study, we consider the billing model and a data center setup of Amazon Elastic Compute Cloud service and estimate the processing time of MME processing instances for different LTE control procedures experimentally. For the considered setup, our results show that the vMME is scalable for signaling workloads up to 37 000 LTE control procedures per second for a target mean system delay of 1 ms. The system design and database performance assumed imposes this limit in the system scalability.This work was supported in part by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (project TIN2013-46223-P) and in part by the Spanish Ministry of Education, Culture, and Sport under FPU Grant 13/04833

Clustering the Unknown - The Youtube Case

Recent stringent end-user security and privacy requirements caused the dramatic rise of encrypted video streams in which YouTube encrypted traffic is one of the most prevalent. Regardless of their encrypted nature, metadata derived from such traffic flows can be utilized to identify the title of a video, thus enabling the classification of video streams into a single video title using a given video title set. Nonetheless, scenarios where no video title set is present and a supervised approach is not feasible, are both frequent and challenging. In this paper we go beyond previous studies and demonstrate the feasibility of clustering unknown video streams into subgroups although no information is available about the title name. We address this problem by exploring Natural Language Processing (NLP) formulations and Word2vec techniques to compose a novel statistical feature in order to further cluster unknown video streams. Through our experimental results over real datasets we demonstrate that our methodology is capable to cluster 72 video titles out of 100 video titles from a dataset of 10,000 video streams. Thus, we argue that the proposed methodology could sufficiently contribute to the newly rising and demanding domain of encrypted Internet traffic classification