On Managing Quality of Experience of Multiple Video Streams in Wireless Networks

Abstract

Abstract-Managing the Quality-of-Experience (QoE) of video streaming for wireless clients is becoming increasingly important due to the rapid growth of video traffic on wireless networks. The inherent variability of the wireless channel as well as the variable bit rate (VBR) of the compressed video streams make managing the QoE a challenging problem. Prior work has studied this problem in the context of transmitting a single video stream. In this paper, we investigate multiplexing schemes to transmit multiple video streams from a base station to mobile clients that use number of playout stalls as a performance metric.In this context, we present an epoch-by-epoch framework to fairly allocate wireless transmission slots to streaming videos. In each epoch our scheme essentially reduces the vulnerability to stalling by allocating slots to videos in a way that maximizes the minimum 'playout lead' across all videos. Next, we show that the problem of allocating slots fairly is NP-complete even for a constant number of videos. We then present a fast leadaware greedy algorithm for the problem. Our choice of greedy algorithm is motivated by the fact that this algorithm is optimal when the channel quality of a user remains unchanged within an epoch (but different users may experience different channel quality). Moreover our experimental results based on public MPEG-4 video traces and wireless channel traces collected from a WiMAX test-bed show that the greedy approach performs a fair distribution of stalls across the clients when compared to other algorithms, while still maintaining similar or lower average number of stalls per client.

A survey on QoE-oriented wireless resources scheduling

“…This methodology, which is intended to run on top of traditional scheduling algorithms, is not proactive regarding overall QoE improvement, i.e., instead of aiming at QoE amelioration for all users at all scheduling instances, this scheme only prioritizes video flows when QoE impairment, namely a playout stall, might be imminent as a result of a buffer that is becoming empty. Another scheduling method that attempts to avoid QoE degradation is presented in (Seetharam et al, 2015), in which the goal is to maximize the minimum buffered video playout time among all users. On the other hand, a more proactive approach can be adopted, in order to also try to enhance the users' overall QoE.…”

Power Control for Wireless VBR Video Streaming: From Optimization to Reinforcement Learning

“…Since overflows are undesirable, the first feature function f 1 (S t , P (t)) indicates if a given P (t) avoids the overflow of the data in the playout buffer at Rx. Additionally, it evaluates if the given action P (t) fulfills the constraint in (2). The function is assigned value "1" if no overflow is caused, and is "0" otherwise.…”

“…Motivated by the above considerations, wireless video streaming has been addressed in several recent studies. For instance, scheduling algorithms to transmit multiple video streams from a base station (BS) to mobile clients were investigated in [2]. With the proposed algorithms, the vulnerability to stalling was reduced by allocating slots to videos in a way that maximizes the minimum playout lead across all videos within an epoch-by-epoch framework.…”

Power control and mode selection for VBR video streaming in D2D networks

“…Recently, scheduling algorithms to transmit multiple video streams from a base station to mobile clients were investigated in [2]. With the proposed algorithms, the vulnerability to stalling was reduced by allocating slots to videos in a way that maximizes the minimum playout lead across all videos with an epoch-by-epoch framework.…”