Predictive buffering for streaming video in 3G networks

Abstract

Abstract-This paper presents a multimedia streaming service in a mobile (3G) environment that, in addition to in-band congestion signals such as packet losses and delay variations, receives congestion cues from a Network Coverage Map Service (NCMS) to make rate-control decisions. The streaming client routinely queries the NCMS to assess the network conditions at future locations along its expected path. The streaming client may ask the streaming server for short-term transmission bursts to increase pre-buffering when it is approaching areas with bad network performance to maintain media quality. If needed, the client may also switch to a different encoding rate (rate-switching) depending on the severity of expected congestion. These notifications are scheduled as late as possible, so that any changes in network conditions and/or changes in user's movements can be taken into account (late scheduling). Using this type of geo-predictive media streaming service we show that the streaming client can provide pause-less playback and better quality of experience to the user.

GTube

“…Recently some video delivery systems associated with location information have been proposed [7,18,22,17]. Curcio et al [7] predicted network bandwidth for the future locations and adjusted the buffering parameters.…”

Adaptive Media Streaming to Mobile Devices: Challenges, Enhancements, and Recommendations

“…When streaming video while commuting, this kind of long-term planning is possible using a location-based bandwidth lookup service for bitrate planning [10,50]. Subsequently, Singh et al [51] proposed a similar geopredictive service as a network coverage map service. To evaluate such a service, we built a time-locationbandwidth database for multiple commute routes and used this for long-term planning of adaptive HTTP streaming sessions.…”

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

“…Hung et al [14] proposed a video streaming control mechanism based on location to overcome signal variations in train tunnels and underground areas. Geo-location frameworks that have the ability to predict future network conditions based on a bandwidth lookup service and similar concepts can be found in [15], [16], [17], [18], [19]. Acharya et al [20] evaluated rateadaptation in a vehicular network based on signal strength and throughput at a location as an indicator for congestion.…”

“…Acharya et al [20] evaluated rateadaptation in a vehicular network based on signal strength and throughput at a location as an indicator for congestion. Curcio et al [15] and Singh et al [16] suggested server-side prediction algorithms for RTP streaming. Curcio et al [15] suggested a framework with a predictive server which obtains: route, speed, location and throughput from the client.…”

“…However, this study was based on simulation rather than real-world data. Singh et al [16] proposed building a Network Coverage Map Service (NCMS) to make rate-control decisions over a Real Time Protocol (RTP) using server-side adaptation algorithm. Singh et al however did not investigate performance on datasets with a higher geographical coverage or more diverse network connectivity conditions.…”