A methodology to evaluate video streaming performance in 802.11e based MANETs

  • Authors:
  • Tim Bohrloch;Carlos T. Calafate;Alvaro Torres;Juan-Carlos Cano;Pietro Manzoni

  • Affiliations:
  • HfT Leipzig, Germany;Universitat Politècnica de València, Spain;Universitat Politècnica de València, Spain;Universitat Politècnica de València, Spain;Universitat Politècnica de València, Spain

  • Venue:
  • ADHOC-NOW'11 Proceedings of the 10th international conference on Ad-hoc, mobile, and wireless networks
  • Year:
  • 2011

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Abstract

Video delivery in mobile ad-hoc networks (MANETs) is an exciting and challenging research field. In the past, most works addressing this issue have resorted to simulation due to the complexity of deploying QoS-enabled testbeds and retrieving video quality indexes in such environments. In this paper we introduce a methodology that allows testing the effectiveness of video codecs in ad-hoc networks. Our methodology relies on a well-defined video quality evaluation framework that is able to combine different video codecs and transmission environments. In particular, our evaluation procedures encompass a preliminary quality assessment, which relies on a point-to-point wireless channel, to establish the general behavior of a video codec under lossy channel conditions, along with tests in static and mobile ad-hoc network environments to determine the impact of factors such as congestion, hop count, and mobility on video quality. To validate our methodology we compare the H.264/AVC and the MPEG-4/ASP video codecs, showing that, in general, the former outperforms the later in terms of video quality, although, for very high loss rates, the differences between both become minimal. Additionally, we show that the number of hops between video transmitter and receiver is a decisive factor affecting performance in the presence of background traffic. Moreover, in mobile scenarios, we find that the impact of congestion and routing delay affects video streaming quality in different manners, being congestion mainly responsible for random losses, while routing delay is usually associated with large loss burst patterns.