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

Abstract

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 multiquality 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 user's 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

“…They estimated the zapping time (i.e., the time required to switch from one tree to another) to be between 0.794 and 1.177 sec. HMAL [25] presented our first adaptive logic algorithm for multicast networks. However, in this paper we presented a different AL mechanism that is based on deep multicast learning as presented in the next section.…”

“…The results show that MAL performs well for low values of β. To demonstrate MAL's efficiency we compared MAL with two ALs: HMAL [25] and ABMM [29]. Figure IV- show that MAL achieves better eMOS [13] (subjective QoE measure) levels.…”

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

“…Most of the current AL methods [2], [3], [4], [5], [6], [7], [8], [9], [10], estimate the next suitable segment based on estimates of previous segments without taking into account the future network characteristics. However, knowledge of geolocation network conditions can enable better decisions.…”