QUIC is the underlying protocol of the next generation HTTP/3, serving as the major vehicle delivering video data nowadays. As a userspace protocol based on UDP, QUIC features low transmission latency and has been widely deployed by content providers. However, the high computational overhead of QUIC shifts system knobs to CPUs in high-bandwidth scenarios. When CPU resources become the constraint, HTTP/3 exhibits even lower throughput than HTTP/1.1. In this paper, we carefully analyze the performance bottleneck of QUIC and find it results from ACK processing, packet sending, and data encryption. By reducing the ACK frequency, activating UDP generic segmentation offload (GSO), and incorporating PicoTLS, a high-performance encryption library, the CPU overhead of QUIC could be effectively reduced in stable network environments. However, simply reducing the ACK frequency also impairs the transmission throughput of QUIC under poor network conditions. To solve this, we develop LiteQUIC, which involves two mechanisms towards alleviating the overhead of ACK processing in addition to GSO and PicoTLS. We evaluate LiteQUIC in the DASH-based video streaming, and the results show that LiteQUIC achieves 1.2× higher average bitrate and 93.3% lower rebuffering time than an optimized version of QUIC with GSO and PicoTLS.

For problems of data compression, gambling, and prediction of individual sequences x/sub 1/, /spl middot//spl middot//spl middot/, x/sub n/ the following questions arise. Given a target family of probability mass functions p(x/sub 1/, /spl middot//spl middot//spl middot/, x/sub n/|/spl theta/), how do we choose a probability mass function q(x/sub 1/, /spl middot//spl middot//spl middot/, x/sub n/) so that it approximately minimizes the maximum regret/belowdisplayskip10ptminus6pt max (log1/q(x/sub 1/, /spl middot//spl middot//spl middot/, x/sub n/)-log1/p(x/sub 1/, /spl middot//spl middot//spl middot/, x/sub n/|/spl theta//spl circ/)) and so that it achieves the best constant C in the asymptotics of the minimax regret, which is of the form (d/2)log(n/2/spl pi/)+C+o(1), where d is the parameter dimension? Are there easily implementable strategies q that achieve those asymptotics? And how does the solution to the worst case sequence problem relate to the solution to the corresponding expectation version min/sub q/ max/sub 0/ E/sub 0/(log1/q(x/sub 1/, /spl middot//spl middot//spl middot/, x/sub n/)-log1/p(x/sub 1/, /spl middot//spl middot//spl middot/, x/sub n/|/spl theta/))? In the discrete memoryless case, with a given alphabet of size m, the Bayes procedure with the Dirichlet(1/2, /spl middot//spl middot//spl middot/, 1/2) prior is asymptotically maximin. Simple modifications of it are shown to be asymptotically minimax. The best constant is C/sub m/=log(/spl Gamma/(1/2)/sup m//(/spl Gamma/(m/2)) which agrees with the logarithm of the integral of the square root of the determinant of the Fisher information. Moreover, our asymptotically optimal strategies for the worst case problem are also asymptotically optimal for the expectation version. Analogous conclusions are given for the case of prediction, gambling, and compression when, for each observation, one has access to side information from an alphabet of size k. In this setting the minimax regret is shown to be k(m-1)/2logn/2/spl pi/k+kC/sub m/+o(1).

Let X/sup n/=(X/sub 1/,...,X/sub n/) be a memoryless source with unknown distribution on a finite alphabet of size k. We identify the asymptotic minimax coding redundancy for this class of sources, and provide a sequence of asymptotically minimax codes. Equivalently, we determine the limiting behavior of the minimax relative entropy min/sub QXn/ max/sub pXn/ D(P/sub Xn//spl par/Q/sub Xn/), where the maximum is over all independent and identically distributed (i.i.d.) source distributions and the minimum is over all joint distributions. We show in this paper that the minimax redundancy minus ((k-1)/2) log(n/(2/spl pi/e)) converges to log/spl int//spl radic/(det I(/spl theta/))d/spl theta/=log (/spl Gamma/(1/2)/sup k///spl Gamma/(k/2)), where I(/spl theta/) is the Fisher information and the integral is over the whole probability simplex. The Bayes strategy using Jeffreys' prior is shown to be asymptotically maximin but not asymptotically minimax in our setting. The boundary risk using Jeffreys' prior is higher than that of interior points. We provide a sequence of modifications of Jeffreys' prior that put some prior mass near the boundaries of the probability simplex to pull down that risk to the asymptotic minimax level in the limit.

Presents two results on the Shannon capacity of M-ary (d,k) codes. First the authors show that 100% efficient fixed-rate codes are impossible for all values of (M,d,k), 0/spl les/d>